Machine Learning in Polymer Research

Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Advanced materials (Weinheim) Ročník 37; číslo 11; s. e2413695 - n/a
Hlavní autori: Ge, Wei, De Silva, Ramindu, Fan, Yanan, Sisson, Scott A., Stenzel, Martina H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Germany Wiley Subscription Services, Inc 01.03.2025
John Wiley and Sons Inc
Predmet:
chemical descriptors
Chemical synthesis
Chemists
Datasets
FAIR data
Machine learning
Mathematicians
Molecular weight distribution
Polymer chemistry
Polymers
Review
chemical descriptors
polymers
machine learning
FAIR data
ISSN:0935-9648, 1521-4095, 1521-4095
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid‐state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer–biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine–human interface are shared. Artificial intelligence (AI) has permeated every aspect of science, including polymer research. Researchers from both fields need to collaborate to understand the challenges and opportunities of each domain. This review is therefore written by mathematicians and polymer chemists to highlight the key research questions polymer chemists aim to address and how machine learning can assist in answering them.
AbstractList Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid‐state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer–biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine–human interface are shared. Artificial intelligence (AI) has permeated every aspect of science, including polymer research. Researchers from both fields need to collaborate to understand the challenges and opportunities of each domain. This review is therefore written by mathematicians and polymer chemists to highlight the key research questions polymer chemists aim to address and how machine learning can assist in answering them.
Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid‐state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer–biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine–human interface are shared.
Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid-state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer-biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine-human interface are shared.Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid-state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer-biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine-human interface are shared.
Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid‐state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer–biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine–human interface are shared. Artificial intelligence (AI) has permeated every aspect of science, including polymer research. Researchers from both fields need to collaborate to understand the challenges and opportunities of each domain. This review is therefore written by mathematicians and polymer chemists to highlight the key research questions polymer chemists aim to address and how machine learning can assist in answering them.
Author Fan, Yanan
De Silva, Ramindu
Sisson, Scott A.
Stenzel, Martina H.
Ge, Wei
AuthorAffiliation 3 Data61, CSIRO Sydney NSW 2015 Australia
2 School of Mathematics and Statistics and UNSW Data Science Hub University of New South Wales Sydney 2052 Australia
1 School of Chemistry University of New South Wales Sydney 2052 Australia
AuthorAffiliation_xml – name: 1 School of Chemistry University of New South Wales Sydney 2052 Australia
– name: 3 Data61, CSIRO Sydney NSW 2015 Australia
– name: 2 School of Mathematics and Statistics and UNSW Data Science Hub University of New South Wales Sydney 2052 Australia
Author_xml – sequence: 1
  givenname: Wei
  surname: Ge
  fullname: Ge, Wei
  organization: University of New South Wales
– sequence: 2
  givenname: Ramindu
  surname: De Silva
  fullname: De Silva, Ramindu
  organization: Data61, CSIRO
– sequence: 3
  givenname: Yanan
  surname: Fan
  fullname: Fan, Yanan
  email: Yanan.Fan@data61.csiro.au
  organization: Data61, CSIRO
– sequence: 4
  givenname: Scott A.
  surname: Sisson
  fullname: Sisson, Scott A.
  email: scott.sisson@unsw.edu.au
  organization: University of New South Wales
– sequence: 5
  givenname: Martina H.
  orcidid: 0000-0002-6433-4419
  surname: Stenzel
  fullname: Stenzel, Martina H.
  email: M.Stenzel@unsw.edu.au
  organization: University of New South Wales
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39924835$$D View this record in MEDLINE/PubMed
BookMark eNqFkc1PGzEQxS0EKiHtlSOK1B64bPD32qcqSqFFCqKqcrcc7ywx2rXBTqjy39dRIAWkqqeRPL_3ZsbvBB2GGAChU4LHBGN6YZvejimmnDCpxQEaEEFJxbEWh2iANROVllwdo5Oc7zHGWmL5AR0zrSlXTAzQlxvrlj7AaAY2BR_uRj6MfsZu00Ma_YJcXt3yIzpqbZfh03MdovnV5Xz6o5rdfr-eTmaV4zURlZUMBG-tAOZEKxqLa1cT0gLFsuWOWEWZbIlkDQfVLBpVqtQg-aImFBo2RF93tg_rRQ-Ng7BKtjMPyfc2bUy03rztBL80d_HJEKIpEwwXh_NnhxQf15BXpvfZQdfZAHGdDSNSYEWE0gX9_A69j-sUynmFqpVWNVd1oc5er7Tf5eUDCzDeAS7FnBO0e4Rgs03IbBMy-4SKgL8TOL-yKx-3J_nu3zK9k_32HWz-M8RMvt1M_mr_ADMhpEI
CitedBy_id crossref_primary_10_1002_marc_202500251
crossref_primary_10_1002_marc_202500361
crossref_primary_10_3390_polym17091172
crossref_primary_10_1021_acs_chemmater_5c00940
crossref_primary_10_3390_polym17131852
crossref_primary_10_1021_acs_jctc_5c00381
crossref_primary_10_1039_D5NR02446C
crossref_primary_10_1016_j_mtphys_2025_101799
crossref_primary_10_1016_j_commatsci_2025_113909
crossref_primary_10_1002_marc_202500380
crossref_primary_10_1007_s11831_025_10325_5
crossref_primary_10_1039_D5PY00148J
crossref_primary_10_1039_D5PY00439J
crossref_primary_10_1016_j_conbuildmat_2025_141828
crossref_primary_10_1080_20550340_2025_2547335
crossref_primary_10_1002_adma_202506243
crossref_primary_10_1021_acspolymersau_5c00057
crossref_primary_10_1039_D5RA02951A
crossref_primary_10_1007_s10118_025_3402_y
crossref_primary_10_1016_j_cis_2025_103612
crossref_primary_10_1038_s41524_025_01767_3
crossref_primary_10_1155_adv_9685300
Cites_doi 10.1016/j.patrec.2017.03.008
10.3934/mbe.2024061
10.1002/(SICI)1521-3773(19981102)37:20<2754::AID-ANIE2754>3.0.CO;2-3
10.1109/ICADIWT.2014.6814687
10.1021/ci010062o
10.1021/ci100050t
10.1016/j.icheatmasstransfer.2015.06.002
10.1186/s40649-019-0069-y
10.1016/j.pmatsci.2024.101282
10.1016/j.ces.2024.119952
10.1088/1674-1056/27/11/118101
10.1002/ceat.201900096
10.1186/s40537-016-0043-6
10.1016/j.apsadv.2021.100068
10.1002/pola.28378
10.1002/bit.21531
10.1007/s00396-009-2035-y
10.1016/j.chemolab.2012.10.003
10.1016/j.polymer.2020.122341
10.1002/ange.201600112
10.1016/j.carbon.2019.02.001
10.1007/s00396-012-2743-6
10.1039/C5MH00282F
10.1016/j.polymer.2021.123558
10.1063/1.474002
10.1023/B:STCO.0000035301.49549.88
10.1186/s40425-018-0416-9
10.1007/978-3-642-41136-6_11
10.1109/TKDE.2017.2720168
10.1016/j.heliyon.2020.e05055
10.1038/s41524-023-01000-z
10.1021/acs.jcim.1c00554
10.20982/tqmp.09.1.p015
10.1007/978-3-030-40245-7_18
10.1002/pi.6345
10.1039/C7SM02472J
10.1007/s10822-016-9938-8
10.1126/sciadv.abc6216
10.1246/bcsj.20210253
10.3390/polym14010082
10.1021/acs.molpharmaceut.3c00880
10.1016/j.compositesb.2023.111099
10.1002/marc.202100400
10.1063/1.1723621
10.1109/TKDE.2019.2953728
10.1016/S0167-9473(01)00065-2
10.1002/app.23112
10.1109/MIPRO.2014.6859735
10.1016/j.crfs.2021.01.002
10.1038/s41598-017-05723-0
10.1038/s41467-023-39868-6
10.1016/j.inffus.2020.07.007
10.1016/j.msec.2008.08.041
10.1080/14686996.2019.1673670
10.1038/s41580-021-00407-0
10.1186/s13321-019-0397-9
10.1145/2939672.2939785
10.1016/j.isci.2020.101922
10.1039/D3PY01028G
10.1038/s42254-021-00314-5
10.3390/ph15111405
10.1021/acsmacrolett.0c00264
10.1021/acs.chemmater.0c03332
10.1063/1.5017661
10.1021/i260029a002
10.1021/acsmacrolett.7b00228
10.1016/j.physd.2019.132306
10.1016/j.matpr.2022.03.051
10.1021/acs.accounts.0c00785
10.1186/1758-2946-3-1
10.3390/polym12010163
10.1002/adts.202100565
10.1002/aisy.202200243
10.1016/j.jpdc.2019.04.008
10.1021/acs.chemmater.3c02358
10.1021/acsami.1c20947
10.1038/npjcompumats.2016.31
10.1016/j.joule.2017.10.006
10.1016/j.commatsci.2021.110278
10.1103/PhysRevLett.114.105503
10.1039/D3PY00246B
10.3390/app132111991
10.1016/j.apmt.2021.101158
10.1021/acspolymersau.2c00053
10.1021/acsapm.0c01376
10.1002/1521-3773(20020617)41:12<2034::AID-ANIE2034>3.0.CO;2-M
10.1021/acs.bioconjchem.8b00804
10.1021/acs.macromol.2c00245
10.1039/D1ME00160D
10.1246/cl.180847
10.1016/S0032-3861(97)01024-0
10.3390/nano11071656
10.1016/j.compchemeng.2023.108356
10.1021/acscentsci.9b00476
10.1021/cr60030a010
10.1016/S0263-8223(00)00179-3
10.1016/j.commatsci.2008.09.027
10.1021/acs.macromol.2c02600
10.1016/j.polymdegradstab.2004.03.006
10.1021/jacsau.3c00275
10.1002/app.45237
10.1088/2632-2153/aba947
10.1021/acs.jpcc.8b02913
10.1186/s13321-021-00502-6
10.1093/nar/gkaa1088
10.1021/acs.jpclett.1c03526
10.1557/mrc.2019.90
10.1021/acs.nanolett.1c04469
10.1038/s41524-017-0056-5
10.1038/s41467-023-40459-8
10.1021/acs.macromol.2c02249
10.1016/j.polymer.2020.123351
10.1021/acs.chemmater.2c02991
10.1038/s41586-021-03819-2
10.1002/wics.1198
10.1021/ci3001925
10.1039/D3PY00314K
10.1023/A:1010933404324
10.1103/PhysRevE.105.035304
10.1007/s00366-020-01226-1
10.1007/s00018-005-5350-6
10.1021/acsabm.2c00962
10.1021/mz4003744
10.1109/TNN.2008.2005605
10.1016/j.porgcoat.2004.05.004
10.1103/PhysRevE.106.014503
10.1021/acs.macromol.4c00835
10.1021/acspolymersau.1c00027
10.1002/anie.202104204
10.1007/978-1-4302-5990-9
10.4028/www.scientific.net/AMR.455-456.436
10.1002/wnan.35
10.1002/pol.20210555
10.1016/j.memsci.2024.123169
10.1557/mrc.2019.32
10.1371/journal.pone.0224365
10.1021/j150415a018
10.1039/D4SM00453A
10.1186/s40537-014-0007-7
10.3390/ijms231810712
10.1080/09669582.2019.1631318
10.1088/2058-6272/aaaade
10.1109/TNNLS.2020.2978386
10.1002/adts.201800069
10.1038/sdata.2016.12
10.1016/j.ces.2022.118326
10.1016/j.jbiotec.2017.07.028
10.1109/TKDE.2009.191
10.1109/ICPR48806.2021.9412492
10.1038/npjcompumats.2016.28
10.1007/s00521-020-05191-0
10.1002/aisy.202070042
10.1021/ci00057a005
10.17849/insm-47-01-31-39.1
10.1201/b18063
10.1021/acs.iecr.1c02142
10.1038/sdata.2016.18
10.1016/j.compscitech.2018.04.017
10.1063/1.1729054
10.1186/s13321-020-00456-1
10.1016/j.inffus.2019.12.001
10.1038/s41524-019-0203-2
10.1016/j.aiopen.2021.01.001
10.1039/C6EE02697D
10.1021/acs.jcim.3c00460
10.1021/acs.molpharmaceut.4c00086
10.1016/j.apsusc.2015.07.066
10.1016/S0895-4356(96)00002-9
10.1002/app.1981.070260429
10.1016/B978-012691360-6/50013-6
10.1039/D0PY01551B
10.1021/jacs.1c08181
10.1021/acs.jpca.3c05892
10.1021/acscentsci.3c00011
10.1109/45.329294
10.1021/acs.jcim.1c01031
10.1109/MSP.2017.2765202
10.1021/acsmacrolett.1c00117
10.1007/s00018-011-0852-x
10.1002/adfm.202309844
10.1007/978-3-319-23871-5
10.1016/j.chemolab.2014.11.008
10.1007/s11433-011-4319-8
10.1021/acssensors.0c00329
10.1016/j.chempr.2022.12.003
10.1016/j.commatsci.2021.110511
10.1039/D1SM01012C
10.1016/j.colsurfa.2005.02.029
10.1002/inf2.12167
10.1007/s10118-022-2716-2
10.1021/acsnano.0c08549
10.1016/j.tibtech.2010.04.002
10.1038/s41578-021-00282-3
10.1021/acs.chemmater.7b03500
10.1039/b909690f
10.1021/acs.macromol.8b01836
10.1021/la2015034
10.1021/jacs.3c12502
10.1016/j.commatsci.2021.110460
10.1039/D2PY01531E
10.1016/j.memsci.2008.04.030
10.1080/08927022.2020.1851028
10.1007/s42107-023-00721-2
10.1016/j.acha.2006.04.006
10.1016/j.compscitech.2022.109818
10.1016/j.spinee.2020.10.006
10.1021/ci060164k
10.1002/pol.1958.1202711515
10.1109/JPROC.2020.3004555
10.1016/j.commatsci.2020.110108
10.1038/s41524-020-0333-6
10.1016/j.commatsci.2022.111859
10.1016/j.commatsci.2004.11.001
10.1021/cr500286d
10.1093/nar/gkv352
10.1021/cr60137a003
10.3390/membranes12090857
10.1021/acs.macromol.7b01379
10.1016/j.compscitech.2022.109293
10.1021/acsami.2c14543
10.1021/acs.jpclett.8b00635
10.1038/s41578-020-0202-4
10.1021/acscentsci.7b00572
10.1021/acs.jcim.9b00587
10.1021/acscentsci.9b00804
10.1002/adfm.202011168
10.1038/s41467-020-15259-z
10.1002/9783527628407
10.1002/minf.201700133
10.1016/j.chemphys.2006.11.029
10.1021/ja501745g
10.1021/ci5004834
10.3389/fmats.2016.00028
10.1039/D2DD00035K
10.1109/MCSE.2023.3342149
10.1063/5.0008026
10.1021/acs.jcim.0c00259
10.1109/2.485891
10.1021/acs.jcim.0c00726
10.3389/fchem.2021.820417
10.1016/j.actbio.2022.02.027
10.1103/PhysRevE.65.021507
10.1016/j.xcrp.2022.100911
10.1016/j.eurpolymj.2016.05.004
10.1016/j.mser.2020.100595
10.1109/IISA52424.2021.9555522
10.1063/5.0023759
10.1021/c160017a018
10.1109/ICIEA.2019.8833686
10.1038/s41598-019-53570-y
10.1039/D0NH00382D
10.1016/j.jmp.2018.03.001
10.1038/s41598-024-59252-8
10.1557/mrc.2019.78
10.1016/j.engappai.2023.107796
10.1016/j.compstruct.2019.111840
10.1016/j.ijfatigue.2006.03.004
10.1021/acsomega.2c04649
10.1021/acsami.3c03298
10.1021/acs.jcim.7b00690
10.1007/978-81-322-3972-7
10.1038/s41427-022-00416-1
10.1186/s13321-018-0258-y
10.1002/app.39059
10.1002/anie.202308838
10.1109/JAS.2017.7510583
10.1021/acspolymersau.1c00050
10.1515/pac-2022-0101
10.1016/j.xcrp.2022.100931
10.1038/s41428-022-00734-9
10.1021/acsanm.4c00430
10.1039/D3PY00395G
10.1007/s11831-021-09700-9
10.1063/5.0131067
10.2147/IJN.S344208
10.1021/acsmacrolett.9b00039
10.1109/ACCESS.2020.3018151
10.1016/j.aej.2023.11.085
10.1039/D4AN00658E
10.1038/s41524-019-0221-0
10.1126/sciadv.aav9784
10.1186/2008-6695-4-7
10.1021/acsnano.8b02454
10.1016/j.commatsci.2023.112479
10.1016/j.ces.2020.116325
10.1002/advs.202101864
10.1021/acs.jpcc.3c00267
10.1038/nmat1543
10.1038/s42256-020-0166-5
10.1016/j.commatsci.2020.110224
10.1016/j.pmatsci.2016.05.001
10.1021/acsami.1c04017
10.3390/polym16010115
10.1021/ma5021209
10.3390/ma12091475
10.1201/9780203910115
10.1021/acsmacrolett.2c00369
10.1021/acspolymersau.1c00035
10.3390/polym14020357
10.1016/j.matpr.2021.02.730
10.1021/jacs.1c05055
10.1039/D1SM00725D
10.1002/adfm.201501919
10.1002/adma.202201809
10.1021/acs.macromol.4c00508
10.1088/1361-651X/aaf8ca
10.1103/PhysRevE.99.043307
10.1038/s41586-018-0337-2
10.1016/j.ijleo.2018.03.121
10.3390/pharmaceutics15020495
10.3390/polym16081049
10.1109/XLOOP49562.2019.00006
10.1021/acs.jctc.3c01385
10.1002/chem.202001505
10.3390/polym14091802
10.1111/j.2517-6161.1996.tb02080.x
10.1021/acs.jcim.9b00807
10.1038/nmat3568
10.1145/3065386
10.1002/macp.202300232
10.1016/0022-5193(79)90085-7
10.1007/978-3-030-05318-5
10.1002/aenm.201900891
10.1093/nar/gky1075
10.1016/j.addr.2022.114172
10.1097/01.psy.0000127692.23278.a9
10.1016/j.compscitech.2006.07.026
10.1016/j.ijhydene.2020.07.265
10.1039/D0ME00020E
10.1002/adem.202101072
10.1021/acspolymersau.2c00037
10.1002/wics.14
10.1609/aaai.v35i1.16072
10.1007/s10924-022-02557-4
10.3390/polym14010026
10.1007/s10822-022-00442-9
10.1038/s41524-024-01328-0
10.1021/acspolymersau.3c00007
10.1016/S0031-3203(01)00178-9
10.1038/s41524-018-0081-z
10.1177/0003702816675361
10.1016/j.spl.2018.02.031
10.1016/j.giant.2023.100171
10.1186/1471-2105-14-106
10.1063/5.0046854
10.1007/978-1-59745-210-6
10.1021/ed100697w
10.1016/j.progpolymsci.2010.01.003
10.1103/PhysRevLett.71.3158
10.1016/j.addr.2020.11.009
10.1371/journal.pone.0161788
10.1021/acsami.1c24715
10.1016/j.renene.2023.01.017
10.1038/s41467-022-35343-w
10.1017/9781108348973
10.1002/anie.201711105
10.1016/j.trechm.2020.11.004
10.1016/j.compbiomed.2020.104134
10.1039/D4SM00590B
10.1002/wcms.1603
10.1142/S0219633608004416
10.1039/D2PY01056A
10.1186/s40537-022-00652-w
10.1021/acsapm.0c00524
10.1016/0098-3004(93)90090-R
10.1038/s41598-021-85601-y
10.1039/D3TA06385B
10.1023/A:1007379606734
10.1038/s41524-023-01034-3
10.1109/EIDWT.2011.13
10.1073/pnas.1607412113
10.1021/acs.jpcb.0c00708
10.3390/polym11081250
10.1016/j.matt.2021.06.036
10.1021/acsmacrolett.1c00521
10.1016/j.matlet.2021.130622
ContentType Journal Article
Copyright 2025 Commonwealth of Australia and The Author(s). Advanced Materials published by Wiley‐VCH GmbH
2025 Commonwealth of Australia and The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
2025. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2025 Commonwealth of Australia and The Author(s). Advanced Materials published by Wiley‐VCH GmbH
– notice: 2025 Commonwealth of Australia and The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
– notice: 2025. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 24P
AAYXX
CITATION
NPM
7SR
8BQ
8FD
JG9
7X8
5PM
DOI 10.1002/adma.202413695
DatabaseName Wiley Online Library Open Access
CrossRef
PubMed
Engineered Materials Abstracts
METADEX
Technology Research Database
Materials Research Database
MEDLINE - Academic
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
PubMed
Materials Research Database
Engineered Materials Abstracts
Technology Research Database
METADEX
MEDLINE - Academic
DatabaseTitleList
CrossRef
Materials Research Database
MEDLINE - Academic

PubMed
Database_xml – sequence: 1
  dbid: 24P
  name: Wiley Online Library Open Access
  url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html
  sourceTypes: Publisher
– sequence: 2
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1521-4095
EndPage n/a
ExternalDocumentID PMC11923530
39924835
10_1002_adma_202413695
ADMA202413695
Genre reviewArticle
Journal Article
Review
GrantInformation_xml – fundername: Australian Research Council
  funderid: FL200100124
– fundername: Australian Research Council
  grantid: FL200100124
GroupedDBID ---
.3N
.GA
05W
0R~
10A
1L6
1OB
1OC
1ZS
23M
24P
33P
3SF
3WU
4.4
4ZD
50Y
50Z
51W
51X
52M
52N
52O
52P
52S
52T
52U
52W
52X
5GY
5VS
66C
6P2
702
7PT
8-0
8-1
8-3
8-4
8-5
8UM
930
A03
AAESR
AAEVG
AAHHS
AAHQN
AAMNL
AANLZ
AAONW
AAXRX
AAYCA
AAZKR
ABCQN
ABCUV
ABIJN
ABJNI
ABLJU
ABPVW
ACAHQ
ACCFJ
ACCZN
ACGFS
ACIWK
ACPOU
ACXBN
ACXQS
ADBBV
ADEOM
ADIZJ
ADKYN
ADMGS
ADOZA
ADXAS
ADZMN
ADZOD
AEEZP
AEIGN
AEIMD
AENEX
AEQDE
AEUYR
AFBPY
AFFPM
AFGKR
AFWVQ
AFZJQ
AHBTC
AITYG
AIURR
AIWBW
AJBDE
AJXKR
ALAGY
ALMA_UNASSIGNED_HOLDINGS
ALUQN
ALVPJ
AMBMR
AMYDB
ATUGU
AUFTA
AZBYB
AZVAB
BAFTC
BDRZF
BFHJK
BHBCM
BMNLL
BMXJE
BNHUX
BROTX
BRXPI
BY8
CS3
D-E
D-F
DCZOG
DPXWK
DR1
DR2
DRFUL
DRSTM
EBS
F00
F01
F04
F5P
G-S
G.N
GNP
GODZA
H.T
H.X
HBH
HGLYW
HHY
HHZ
HZ~
IX1
J0M
JPC
KQQ
LATKE
LAW
LC2
LC3
LEEKS
LH4
LITHE
LOXES
LP6
LP7
LUTES
LYRES
MEWTI
MK4
MRFUL
MRSTM
MSFUL
MSSTM
MXFUL
MXSTM
N04
N05
N9A
NF~
NNB
O66
O9-
OIG
P2P
P2W
P2X
P4D
Q.N
Q11
QB0
QRW
R.K
RNS
ROL
RX1
RYL
SUPJJ
TN5
UB1
UPT
V2E
W8V
W99
WBKPD
WFSAM
WIB
WIH
WIK
WJL
WOHZO
WQJ
WXSBR
WYISQ
XG1
XPP
XV2
YR2
ZZTAW
~02
~IA
~WT
.Y3
31~
53G
6TJ
8WZ
A6W
AAMMB
AANHP
AASGY
AAYXX
ABEML
ACBWZ
ACRPL
ACSCC
ACYXJ
ADMLS
ADNMO
AEFGJ
AETEA
AEYWJ
AFFNX
AGHNM
AGQPQ
AGXDD
AGYGG
AIDQK
AIDYY
AIQQE
ASPBG
AVWKF
AZFZN
CITATION
EJD
FEDTE
FOJGT
HF~
HVGLF
LW6
M6K
NDZJH
O8X
PALCI
RIWAO
RJQFR
SAMSI
WTY
ZY4
AAYOK
ABTAH
NPM
7SR
8BQ
8FD
JG9
7X8
5PM
ID FETCH-LOGICAL-c4715-a63e54fa5e3c5f5da07c711fe206f4c1a8236f163d4e8dbd8d4e69e64b712ed3
IEDL.DBID 24P
ISICitedReferencesCount 39
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001419079100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 0935-9648
1521-4095
IngestDate Tue Nov 04 02:03:24 EST 2025
Fri Jul 11 13:39:27 EDT 2025
Fri Jul 25 21:25:55 EDT 2025
Sun Mar 23 01:27:35 EDT 2025
Sat Nov 29 07:25:08 EST 2025
Tue Nov 18 22:14:51 EST 2025
Thu Mar 20 09:30:38 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 11
Keywords chemical descriptors
polymers
machine learning
FAIR data
Language English
License Attribution-NonCommercial
2025 Commonwealth of Australia and The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c4715-a63e54fa5e3c5f5da07c711fe206f4c1a8236f163d4e8dbd8d4e69e64b712ed3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ORCID 0000-0002-6433-4419
OpenAccessLink https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fadma.202413695
PMID 39924835
PQID 3178987487
PQPubID 2045203
PageCount 47
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_11923530
proquest_miscellaneous_3165081589
proquest_journals_3178987487
pubmed_primary_39924835
crossref_primary_10_1002_adma_202413695
crossref_citationtrail_10_1002_adma_202413695
wiley_primary_10_1002_adma_202413695_ADMA202413695
PublicationCentury 2000
PublicationDate 2025-03-01
PublicationDateYYYYMMDD 2025-03-01
PublicationDate_xml – month: 03
  year: 2025
  text: 2025-03-01
  day: 01
PublicationDecade 2020
PublicationPlace Germany
PublicationPlace_xml – name: Germany
– name: Weinheim
– name: Hoboken
PublicationTitle Advanced materials (Weinheim)
PublicationTitleAlternate Adv Mater
PublicationYear 2025
Publisher Wiley Subscription Services, Inc
John Wiley and Sons Inc
Publisher_xml – name: Wiley Subscription Services, Inc
– name: John Wiley and Sons Inc
References 2015; 140
2017; 85
2019; 11
2019; 12
2013; 129
2019; 14
2016; 30
2023; 224
2020; 14
2020; 12
2024; 34
2020; 11
2024; 36
2021; 71
2001; 45
1997; 9
2017; 71
2019; 20
2000; 13
2023; 216
2019; 27
2024; 20
2024; 21
1967; 14
2001; 53
21
2021; 46
2021; 49
2019; 30
2022; 94
2021; 304
2023; 204
2024; 10
2020; 33
2024; 12
2020; 32
2024; 14
2011; 3
2017; 134
2024; 15
2024; 16
2016; 11
2016; 4
2017; 50
2021; 59
2021; 54
2004; 51
2016; 2
2016; 3
2022; 183
2022; 3
2015; 115
2022; 5
2015; 114
2015; 355
2022; 7
2017; 55
2002; 65
2022; 9
2019; 48
2019; 47
2020; 28
2020; 26
1994; 13
2017; 261
2022; 1
2022; 2
2020; 21
2021; 61
2021; 60
2006; 101
2004; 66
2023; 35
2020; 64
2024; 269
2019; 52
2024; 149
2017; 47
2020; 60
2021; 128
2019; 59
2020; 127
2020; 128
2008; 7
2020; 57
2024; 144
2016; 225
2020; 124
2024; 146
2008; 75
2020; 8
2007; 29
1969; 8
2020; 6
2020; 5
2017; 30
2019; 60
1996; 29
2005; 260
2023; 24
2013; 14
2020; 3
2023; 25
1996; 28
2020; 2
2020; 1
1963; 34
2001
2002; 41
2021; 596
2002; 42
2000
1993; 71
2013; 12
2024; 712
2021; 118
2020; 9
2016; 113
2020; 46
2020; 45
2020; 43
2021; 231
2012; 455
2014; 54
2021; 9
2002; 38
2015; 2
2021; 109
2004; 85
2021; 6
2021; 87
2023; 13
2023; 14
2021; 4
2021; 3
2012
2011
2023; 15
2010
2023; 16
2002; 35
2009
2008
2007
2006
2016; 128
2017; 29
2008; 99
2024; 57
2021; 220
2002
2021; 94
1996; 58
2021; 1
2008; 320
1942; 46
2023
2022
2018; 559
2021
2021; 214
2020
2022, preprint
2017; 10
2019
2020; 236
2018
2017
2016
2020; 68
2015
2023， 347
2014
2013
2009; 2
1990; 792
2009; 1
1996; 49
2007; 47
2024; 291
2018; 58
2018; 57
2018; 165
2018; 162
2013; 2
2019; 99
1983; 5
2022; 23
2022; 24
2011; 54
2022; 22
2014; 136
2022; 29
2013; 9
2018; 6
2010; 22
2023; 62
2018; 9
2007; 332
2023; 63
2009; 10
2018; 4
2006; 21
2022; 40
2010; 28
2024; 7
1965; 5
2023; 177
2022; 34
2022; 36
2022; 30
2009; 19
2007; 67
2018; 37
1942; 10
2018; 35
2019; 8
2019; 9
2006; 50
2019; 6
2019; 5
2023; 56
2010; 35
2019; 2
2014; 47
1997; 28
1981; 26
1993
2021; 144
2021; 143
1995; 4
2018; 20
2018; 27
2014; 43
2022; 220
1958; 27
2015; 66
1988; 28
2022; 12
2024; 130
2022; 13
2022; 14
2022; 15
1997; 38
2024; 86
1931; 8
2022; 11
2018; 12
2018; 10
2022; 106
2022; 105
2012; 119
1979; 81
2022; 17
2010; 50
2018; 14
2021; 25
2009; 45
2017; 6
2021; 24
2017; 7
2018; 122
2021; 21
2017; 1
2017; 3
2017; 4
2023; 5
2023; 7
2023; 267
2023; 9
2020; 404
2023; 1
2011; 12
2023; 3
2012; 52
2021; 35
2021; 38
1997; 106
2021; 32
2006; 63
2021; 31
2021; 33
2012; 291
2015; 43
2018; 136
2009; 287
2021; 194
2016; 83
2021; 196
2016; 80
2012; 69
2011; 27
1950; 3
2005; 34
2021; 190
2009; 20
2018; 148
2023; 127
2006; 5
2019; 146
2022; 43
2006; 1
2021; 188
2021; 187
2009; 29
2021; 14
2021; 13
1998; 37
2022; 143
2015; 25
2021; 10
2015; 28
1993; 19
2015; 27
2017; 90
1949; 44
2021; 12
2021; 11
2023; 230
2004; 14
2021; 17
2020; 193
2023; 231
2021; 171
2022; 57
2022; 55
2012; 4
2019; 131
2012; 9
e_1_2_8_241_1
e_1_2_8_287_1
e_1_2_8_264_1
e_1_2_8_309_1
e_1_2_8_26_1
e_1_2_8_49_1
e_1_2_8_407_1
e_1_2_8_132_1
e_1_2_8_155_1
e_1_2_8_178_1
e_1_2_8_385_1
Sivanandam S. (e_1_2_8_302_1) 2008
e_1_2_8_9_1
e_1_2_8_230_1
e_1_2_8_276_1
e_1_2_8_253_1
e_1_2_8_15_1
e_1_2_8_38_1
e_1_2_8_299_1
e_1_2_8_120_1
e_1_2_8_143_1
e_1_2_8_166_1
e_1_2_8_189_1
e_1_2_8_91_1
e_1_2_8_429_1
e_1_2_8_374_1
e_1_2_8_397_1
e_1_2_8_351_1
e_1_2_8_242_1
e_1_2_8_265_1
e_1_2_8_25_1
e_1_2_8_48_1
Gorbach N. S. (e_1_2_8_244_1) 2017
e_1_2_8_408_1
e_1_2_8_288_1
Nield T. (e_1_2_8_114_1) 2022
e_1_2_8_133_1
e_1_2_8_179_1
e_1_2_8_110_1
e_1_2_8_386_1
e_1_2_8_363_1
e_1_2_8_340_1
e_1_2_8_156_1
e_1_2_8_231_1
e_1_2_8_14_1
e_1_2_8_37_1
e_1_2_8_277_1
e_1_2_8_428_1
e_1_2_8_144_1
e_1_2_8_90_1
e_1_2_8_121_1
e_1_2_8_375_1
e_1_2_8_352_1
Hamilton W. (e_1_2_8_220_1) 2017; 30
e_1_2_8_398_1
e_1_2_8_167_1
e_1_2_8_28_1
e_1_2_8_243_1
e_1_2_8_409_1
e_1_2_8_266_1
e_1_2_8_289_1
e_1_2_8_81_1
e_1_2_8_439_1
e_1_2_8_111_1
e_1_2_8_7_1
e_1_2_8_341_1
e_1_2_8_364_1
Song Y. Y. (e_1_2_8_184_1) 2015; 27
e_1_2_8_387_1
e_1_2_8_134_1
e_1_2_8_157_1
e_1_2_8_17_1
e_1_2_8_232_1
e_1_2_8_255_1
e_1_2_8_278_1
e_1_2_8_427_1
e_1_2_8_70_1
e_1_2_8_122_1
e_1_2_8_353_1
e_1_2_8_376_1
e_1_2_8_399_1
e_1_2_8_168_1
e_1_2_8_93_1
e_1_2_8_221_1
e_1_2_8_27_1
Saxena A. (e_1_2_8_362_1) 2023
e_1_2_8_267_1
e_1_2_8_438_1
e_1_2_8_80_1
e_1_2_8_342_1
e_1_2_8_8_1
e_1_2_8_365_1
e_1_2_8_112_1
e_1_2_8_158_1
e_1_2_8_388_1
e_1_2_8_135_1
e_1_2_8_39_1
e_1_2_8_210_1
e_1_2_8_16_1
e_1_2_8_233_1
e_1_2_8_256_1
e_1_2_8_279_1
e_1_2_8_426_1
e_1_2_8_92_1
e_1_2_8_100_1
e_1_2_8_354_1
Ehrenstein G. W. (e_1_2_8_4_1) 2012
e_1_2_8_123_1
e_1_2_8_169_1
e_1_2_8_377_1
e_1_2_8_146_1
e_1_2_8_283_1
e_1_2_8_207_1
Felder R. M. (e_1_2_8_159_1) 2009; 2
e_1_2_8_151_1
e_1_2_8_381_1
e_1_2_8_174_1
e_1_2_8_197_1
e_1_2_8_328_1
e_1_2_8_60_1
e_1_2_8_83_1
e_1_2_8_305_1
e_1_2_8_19_1
e_1_2_8_295_1
e_1_2_8_109_1
e_1_2_8_272_1
e_1_2_8_440_1
e_1_2_8_415_1
e_1_2_8_95_1
e_1_2_8_219_1
e_1_2_8_162_1
e_1_2_8_370_1
e_1_2_8_393_1
e_1_2_8_185_1
e_1_2_8_72_1
e_1_2_8_317_1
e_1_2_8_29_1
e_1_2_8_284_1
e_1_2_8_329_1
e_1_2_8_261_1
e_1_2_8_404_1
e_1_2_8_152_1
e_1_2_8_208_1
e_1_2_8_382_1
e_1_2_8_175_1
e_1_2_8_306_1
e_1_2_8_82_1
e_1_2_8_198_1
e_1_2_8_18_1
e_1_2_8_318_1
e_1_2_8_273_1
e_1_2_8_296_1
e_1_2_8_250_1
Ali J. (e_1_2_8_254_1) 2012; 9
Massimo A. (e_1_2_8_130_1) 2021; 6
e_1_2_8_416_1
e_1_2_8_94_1
e_1_2_8_394_1
e_1_2_8_163_1
e_1_2_8_140_1
e_1_2_8_186_1
e_1_2_8_71_1
e_1_2_8_262_1
e_1_2_8_307_1
e_1_2_8_285_1
Gerdolle D. A. (e_1_2_8_235_1) 2008; 75
e_1_2_8_360_1
e_1_2_8_383_1
e_1_2_8_405_1
e_1_2_8_153_1
e_1_2_8_209_1
Schölkopf B. (e_1_2_8_145_1) 2000; 13
e_1_2_8_62_1
e_1_2_8_85_1
Medsker L. (e_1_2_8_173_1) 2001
e_1_2_8_176_1
e_1_2_8_199_1
e_1_2_8_319_1
e_1_2_8_251_1
e_1_2_8_297_1
e_1_2_8_274_1
e_1_2_8_417_1
e_1_2_8_372_1
e_1_2_8_395_1
e_1_2_8_141_1
e_1_2_8_164_1
e_1_2_8_97_1
e_1_2_8_51_1
e_1_2_8_74_1
e_1_2_8_187_1
Michael C. J. N. (e_1_2_8_238_1) 1983
e_1_2_8_240_1
e_1_2_8_263_1
e_1_2_8_286_1
e_1_2_8_308_1
Pedregosa F. (e_1_2_8_371_1) 2011; 12
e_1_2_8_406_1
e_1_2_8_154_1
e_1_2_8_384_1
Novaković J. D. (e_1_2_8_129_1) 2017; 7
e_1_2_8_131_1
e_1_2_8_361_1
e_1_2_8_84_1
e_1_2_8_61_1
e_1_2_8_252_1
e_1_2_8_275_1
e_1_2_8_298_1
Agresti A. (e_1_2_8_115_1) 1990
He H. (e_1_2_8_53_1); 21
Babyak M. A. (e_1_2_8_260_1) 2004; 66
e_1_2_8_418_1
e_1_2_8_165_1
e_1_2_8_373_1
e_1_2_8_96_1
e_1_2_8_142_1
e_1_2_8_396_1
e_1_2_8_350_1
e_1_2_8_73_1
e_1_2_8_50_1
e_1_2_8_188_1
e_1_2_8_249_1
e_1_2_8_433_1
e_1_2_8_226_1
Li Zixin Z. N. (e_1_2_8_55_1) 2020; 2
e_1_2_8_117_1
e_1_2_8_170_1
e_1_2_8_193_1
e_1_2_8_290_1
Hansen C. M. (e_1_2_8_331_1) 1967
e_1_2_8_324_1
e_1_2_8_64_1
e_1_2_8_87_1
e_1_2_8_301_1
e_1_2_8_1_1
e_1_2_8_41_1
e_1_2_8_347_1
e_1_2_8_291_1
e_1_2_8_215_1
e_1_2_8_411_1
e_1_2_8_99_1
e_1_2_8_105_1
e_1_2_8_181_1
Carreira‐Perpinán M. A. (e_1_2_8_205_1) 1997; 9
e_1_2_8_76_1
e_1_2_8_313_1
e_1_2_8_30_1
e_1_2_8_336_1
e_1_2_8_359_1
e_1_2_8_280_1
e_1_2_8_432_1
Van Der Maaten L. (e_1_2_8_203_1) 2009; 10
e_1_2_8_227_1
e_1_2_8_204_1
e_1_2_8_2_1
Hildebrand J. H. (e_1_2_8_330_1) 1950
e_1_2_8_400_1
e_1_2_8_171_1
e_1_2_8_86_1
e_1_2_8_118_1
e_1_2_8_194_1
e_1_2_8_63_1
e_1_2_8_325_1
e_1_2_8_40_1
e_1_2_8_348_1
e_1_2_8_292_1
e_1_2_8_420_1
e_1_2_8_443_1
e_1_2_8_239_1
e_1_2_8_216_1
e_1_2_8_412_1
e_1_2_8_98_1
e_1_2_8_390_1
e_1_2_8_106_1
e_1_2_8_182_1
e_1_2_8_75_1
e_1_2_8_52_1
e_1_2_8_314_1
e_1_2_8_337_1
e_1_2_8_281_1
e_1_2_8_431_1
e_1_2_8_228_1
e_1_2_8_401_1
e_1_2_8_20_1
e_1_2_8_43_1
e_1_2_8_66_1
e_1_2_8_89_1
Jones R. G. (e_1_2_8_421_1) 2008
e_1_2_8_119_1
e_1_2_8_172_1
e_1_2_8_195_1
e_1_2_8_303_1
e_1_2_8_349_1
e_1_2_8_326_1
Kotu V. (e_1_2_8_445_1) 2015
e_1_2_8_293_1
e_1_2_8_270_1
e_1_2_8_442_1
e_1_2_8_217_1
e_1_2_8_413_1
e_1_2_8_391_1
e_1_2_8_32_1
e_1_2_8_78_1
e_1_2_8_107_1
e_1_2_8_183_1
e_1_2_8_315_1
e_1_2_8_338_1
Haldurai L. (e_1_2_8_323_1) 2016; 4
e_1_2_8_282_1
e_1_2_8_430_1
Rasmussen C. E. (e_1_2_8_403_1) 2006
e_1_2_8_229_1
e_1_2_8_206_1
e_1_2_8_402_1
e_1_2_8_380_1
e_1_2_8_42_1
e_1_2_8_88_1
e_1_2_8_65_1
e_1_2_8_304_1
e_1_2_8_327_1
e_1_2_8_196_1
e_1_2_8_271_1
e_1_2_8_294_1
e_1_2_8_441_1
e_1_2_8_218_1
e_1_2_8_414_1
e_1_2_8_161_1
Wang T. (e_1_2_8_177_1) 2012
e_1_2_8_392_1
e_1_2_8_31_1
e_1_2_8_77_1
e_1_2_8_54_1
e_1_2_8_108_1
(e_1_2_8_150_1) 2006
e_1_2_8_339_1
e_1_2_8_316_1
e_1_2_8_68_1
e_1_2_8_222_1
e_1_2_8_245_1
e_1_2_8_437_1
e_1_2_8_268_1
e_1_2_8_5_1
e_1_2_8_320_1
e_1_2_8_22_1
e_1_2_8_45_1
e_1_2_8_366_1
e_1_2_8_113_1
e_1_2_8_136_1
e_1_2_8_57_1
Oka H. (e_1_2_8_419_1) 2021; 1
Kaur R. (e_1_2_8_444_1) 2020; 68
e_1_2_8_211_1
e_1_2_8_234_1
e_1_2_8_425_1
e_1_2_8_257_1
e_1_2_8_448_1
e_1_2_8_11_1
e_1_2_8_34_1
e_1_2_8_332_1
e_1_2_8_355_1
e_1_2_8_101_1
e_1_2_8_124_1
e_1_2_8_147_1
e_1_2_8_378_1
e_1_2_8_200_1
e_1_2_8_436_1
e_1_2_8_246_1
e_1_2_8_269_1
e_1_2_8_6_1
e_1_2_8_21_1
e_1_2_8_67_1
e_1_2_8_321_1
e_1_2_8_367_1
e_1_2_8_44_1
e_1_2_8_344_1
e_1_2_8_137_1
e_1_2_8_79_1
e_1_2_8_212_1
e_1_2_8_424_1
e_1_2_8_447_1
e_1_2_8_258_1
e_1_2_8_10_1
e_1_2_8_56_1
e_1_2_8_310_1
e_1_2_8_356_1
e_1_2_8_33_1
e_1_2_8_333_1
e_1_2_8_102_1
e_1_2_8_148_1
e_1_2_8_125_1
e_1_2_8_379_1
e_1_2_8_24_1
e_1_2_8_47_1
Upadhya R. (e_1_2_8_389_1) 2022
Duvenaud D. K. (e_1_2_8_223_1) 2015; 28
e_1_2_8_224_1
e_1_2_8_201_1
e_1_2_8_435_1
e_1_2_8_247_1
e_1_2_8_3_1
e_1_2_8_322_1
e_1_2_8_345_1
e_1_2_8_368_1
e_1_2_8_138_1
Drucker H. (e_1_2_8_160_1) 1996; 28
e_1_2_8_13_1
e_1_2_8_36_1
e_1_2_8_59_1
e_1_2_8_190_1
e_1_2_8_213_1
e_1_2_8_259_1
e_1_2_8_423_1
e_1_2_8_446_1
e_1_2_8_236_1
e_1_2_8_311_1
e_1_2_8_334_1
e_1_2_8_357_1
e_1_2_8_149_1
e_1_2_8_103_1
e_1_2_8_126_1
e_1_2_8_46_1
e_1_2_8_69_1
e_1_2_8_180_1
e_1_2_8_202_1
e_1_2_8_225_1
e_1_2_8_248_1
Nasrin T. (e_1_2_8_128_1) 2023; 62
e_1_2_8_434_1
Arthur D. (e_1_2_8_343_1) 2007
e_1_2_8_192_1
e_1_2_8_300_1
e_1_2_8_116_1
e_1_2_8_346_1
e_1_2_8_23_1
e_1_2_8_139_1
e_1_2_8_369_1
e_1_2_8_35_1
e_1_2_8_58_1
e_1_2_8_191_1
e_1_2_8_214_1
e_1_2_8_237_1
e_1_2_8_422_1
e_1_2_8_410_1
e_1_2_8_127_1
e_1_2_8_335_1
e_1_2_8_12_1
e_1_2_8_312_1
e_1_2_8_104_1
e_1_2_8_358_1
References_xml – volume: 559
  start-page: 547
  year: 2018
  publication-title: Nature
– volume: 14
  year: 2022
  publication-title: ACS Appl. Mater. Interfaces
– volume: 11
  start-page: 1117
  year: 2022
  publication-title: ACS Macro Lett.
– volume: 128
  start-page: 4576
  year: 2016
  publication-title: Angew. Chem., Int. Ed.
– volume: 14
  year: 2019
  publication-title: PLoS One
– volume: 35
  start-page: 1560
  year: 2023
  publication-title: Chem. Mater.
– volume: 15
  start-page: 1273
  year: 2024
  publication-title: Polym. Chem.
– volume: 10
  start-page: 749
  year: 2021
  publication-title: ACS Macro Lett.
– volume: 24
  year: 2022
  publication-title: Adv. Eng. Mater.
– volume: 42
  start-page: 232
  year: 2002
  publication-title: J. Chem. Inf. Comput. Sci.
– volume: 46
  start-page: 5838
  year: 2021
  publication-title: Mater. Today: Proc.
– volume: 9
  year: 2021
  publication-title: Front. Chem.
– volume: 37
  start-page: 2754
  year: 1998
  publication-title: Angew. Chem., Int. Ed.
– volume: 231
  year: 2021
  publication-title: Chem. Eng. Sci.
– volume: 106
  start-page: 9327
  year: 1997
  publication-title: J. Chem. Phys.
– volume: 287
  start-page: 811
  year: 2009
  publication-title: Colloid Polym. Sci.
– year: 2014
– volume: 35
  start-page: 459
  year: 2010
  publication-title: Prog. Polym. Sci.
– volume: 3
  start-page: 9
  year: 2016
  publication-title: J. Big Data
– volume: 75
  start-page: 125
  year: 2008
  publication-title: J. Dent. Child.
– volume: 11
  start-page: 74
  year: 2019
  publication-title: J. Cheminf.
– volume: 4
  start-page: 2702
  year: 2021
  publication-title: Matter
– volume: 85
  start-page: 667
  year: 2004
  publication-title: Polym. Degrad. Stab.
– volume: 12
  start-page: 1603
  year: 2022
  publication-title: WIREs Comput. Mol. Sci.
– volume: 12
  start-page: 2209
  year: 2024
  publication-title: J. Mater. Chem. A
– volume: 24
  start-page: 3399
  year: 2023
  publication-title: Asian J. Civ. Eng.
– volume: 50
  start-page: 742
  year: 2010
  publication-title: J. Chem. Inf. Model.
– start-page: 323
  year: 2000
  end-page: 352
– volume: 54
  start-page: 878
  year: 2011
  publication-title: Sci. China: Phys., Mech. Astron.
– volume: 127
  year: 2020
  publication-title: J. Appl. Phys.
– volume: 28
  start-page: 31
  year: 1988
  publication-title: J. Chem. Inf. Comput. Sci.
– volume: 105
  year: 2022
  publication-title: Phys. Rev. E
– volume: 46
  start-page: 1509
  year: 2020
  publication-title: Mol. Simul.
– volume: 6
  year: 2021
  publication-title: Mach. Learn. Appl.
– volume: 64
  start-page: 205
  year: 2020
  publication-title: Inf. Fusion
– volume: 1
  start-page: 57
  year: 2020
  publication-title: AI Open
– volume: 4
  start-page: 139
  year: 2016
  publication-title: Int. J. Comput. Sci. Eng.
– volume: 62
  year: 2023
  publication-title: Angew. Chem., Int. Ed.
– volume: 9
  start-page: 2639
  year: 2018
  publication-title: J. Phys. Chem. Lett.
– year: 2002
– volume: 26
  start-page: 9982
  year: 2020
  publication-title: Chem.‐ Eur. J.
– volume: 455
  start-page: 436
  year: 2012
  publication-title: Adv. Mater. Res.
– volume: 30
  start-page: 4800
  year: 2022
  publication-title: J. Polym. Environ.
– volume: 9
  start-page: 924
  year: 2023
  publication-title: Chem
– volume: 87
  start-page: 1123
  year: 2021
  publication-title: J. Chem. Educ.
– volume: 60
  start-page: 4684
  year: 2020
  publication-title: J. Chem. Inf. Model.
– volume: 404
  year: 2020
  publication-title: Phys. D
– volume: 6
  start-page: 1078
  year: 2017
  publication-title: ACS Macro Lett.
– volume: 53
  start-page: 65
  year: 2001
  publication-title: Compos. Struct
– volume: 27
  year: 2018
  publication-title: Chin. Phys. B
– volume: 17
  start-page: 7607
  year: 2021
  publication-title: Soft Matter
– volume: 25
  year: 2021
  publication-title: Appl. Mater. Today
– volume: 2
  start-page: 775
  year: 2013
  publication-title: ACS Macro Lett.
– start-page: 306
  year: 2017
  publication-title: Pattern Recognit.
– volume: 21
  start-page: 1413
  year: 2024
  publication-title: Math. Biosci. Eng.
– volume: 86
  start-page: 405
  year: 2024
  publication-title: Alexandria Eng. J.
– volume: 14
  start-page: 1327
  year: 2018
  publication-title: Soft Matter
– volume: 3
  year: 1950
– volume: 190
  year: 2021
  publication-title: Comput. Mater. Sci.
– volume: 14
  start-page: 4838
  year: 2023
  publication-title: Nat. Commun.
– volume: 43
  year: 2022
  publication-title: Macromol. Rapid Commun.
– volume: 22
  start-page: 2660
  year: 2022
  publication-title: Nano Lett.
– volume: 12
  start-page: 857
  year: 2022
  publication-title: Membranes
– volume: 62
  start-page: 6239
  year: 2023
  publication-title: J. Polym. Sci.
– volume: 3
  start-page: 2510
  year: 2023
  publication-title: JACS Au
– volume: 25
  start-page: 4
  year: 2023
  publication-title: Comput. Sci. Eng.
– volume: 36
  start-page: 355
  year: 2022
  publication-title: J. Comput.‐Aided Mol. Des.
– volume: 9
  start-page: 1
  year: 1997
  publication-title: University of Sheffield. Tech. Rep.
– volume: 106
  year: 2022
  publication-title: Phys. Rev. E
– volume: 20
  start-page: 4905
  year: 2024
  publication-title: Soft Matter
– volume: 320
  start-page: 390
  year: 2008
  publication-title: J. Membr. Sci.
– volume: 67
  start-page: 168
  year: 2007
  publication-title: Compos. Sci. Technol.
– volume: 291
  start-page: 551
  year: 2012
  publication-title: Colloid Polym. Sci.
– volume: 49
  start-page: 8
  year: 2021
  publication-title: Nucleic Acids Res.
– volume: 60
  start-page: 4457
  year: 2020
  publication-title: J. Chem. Inf. Model.
– volume: 40
  start-page: 834
  year: 2022
  publication-title: Chin. J. Polym. Sci.
– year: 2015
– volume: 115
  start-page: 1106
  year: 2015
  publication-title: Chem. Rev.
– volume: 99
  year: 2019
  publication-title: Phys. Rev. E
– volume: 56
  start-page: 3945
  year: 2023
  publication-title: Macromolecules
– volume: 61
  start-page: 5395
  year: 2021
  publication-title: J. Chem. Inf. Model.
– volume: 55
  start-page: 117
  year: 2022
  publication-title: Polym. J.
– volume: 5
  start-page: 39
  year: 2006
  publication-title: Nat. Mater.
– volume: 27
  start-page: 130
  year: 2015
  publication-title: Shanghai Arch. Psychiatry
– volume: 47
  start-page: 150
  year: 2007
  publication-title: J. Chem. Inf. Model.
– volume: 14
  year: 1967
– volume: 94
  start-page: 2410
  year: 2021
  publication-title: Bull. Chem. Soc. Jpn.
– volume: 2
  start-page: 8
  year: 2022
  publication-title: ACS Polym. Au
– volume: 2
  year: 2020
  publication-title: Adv. Intell. Syst.
– volume: 3
  start-page: 141
  year: 2023
  publication-title: ACS Polym. Au
– volume: 30
  year: 2017
  publication-title: Adv. Neural Inf. Process. Syst.
– start-page: 12
  year: 2021
  end-page: 16
– volume: 71
  start-page: 3158
  year: 1993
  publication-title: Phys. Rev. Lett.
– volume: 127
  start-page: 6179
  year: 2023
  publication-title: J. Phys. Chem. C
– volume: 2
  year: 2016
  publication-title: npj Comput. Mater.
– volume: 17
  start-page: 7697
  year: 2021
  publication-title: Soft Matter
– volume: 3
  year: 2016
  publication-title: Sci. Data
– volume: 43
  year: 2014
– volume: 29
  start-page: 3341
  year: 2022
  publication-title: Arch Computat Methods Eng
– volume: 131
  start-page: 1
  year: 2019
  publication-title: J. Parallel Distrib. Comput.
– volume: 59
  start-page: 5013
  year: 2019
  publication-title: J. Chem. Inf. Model.
– volume: 596
  start-page: 583
  year: 2021
  publication-title: Nature
– volume: 127
  start-page: 9863
  year: 2023
  publication-title: J. Phys. Chem. A
– volume: 81
  start-page: 123
  year: 1979
  publication-title: J. Theory Biol.
– volume: 20
  start-page: 61
  year: 2009
  publication-title: IEEE Trans. Neural Netw.
– volume: 14
  start-page: 199
  year: 2004
  publication-title: Stat. Comput.
– volume: 12
  year: 2021
  publication-title: J. Phys. Chem. Lett.
– volume: 3
  start-page: 226
  year: 2016
  publication-title: Mater. Horiz.
– volume: 59
  start-page: 2613
  year: 2021
  publication-title: J. Polym. Sci.
– volume: 225
  year: 2016
– volume: 29
  start-page: 2318
  year: 2017
  publication-title: IEEE Trans. Knowl. Data Eng.
– volume: 71
  start-page: 537
  year: 2021
  publication-title: Polym. Int.
– volume: 4
  year: 2021
  publication-title: Appl. Surf. Sci.
– volume: 4
  start-page: 28
  year: 2021
  publication-title: Curr. Res. Food Sci.
– volume: 148
  year: 2018
  publication-title: J. Chem. Phys.
– start-page: 505
  year: 2019
  end-page: 510
– start-page: 785
  year: 2016
  end-page: 794
– volume: 3
  year: 2016
  publication-title: Front. Mater.
– volume: 50
  start-page: 7410
  year: 2017
  publication-title: Macromolecules
– volume: 35
  start-page: 2279
  year: 2002
  publication-title: Pattern Recognit
– volume: 21
  start-page: 333
  year: 2024
  publication-title: Mol. Pharmaceutics
– volume: 162
  start-page: 146
  year: 2018
  publication-title: Compos. Sci. Technol.
– volume: 144
  year: 2024
  publication-title: Prog. Mater. Sci.
– volume: 5
  year: 2023
  publication-title: Adv. Intell. Syst.
– volume: 30
  start-page: 503
  year: 2019
  publication-title: Bioconjugate Chem.
– volume: 119
  start-page: 21
  year: 2012
  publication-title: Chemom. Intell. Lab. Syst.
– volume: 90
  start-page: 8
  year: 2017
  publication-title: Pattern Recognit. Lett.
– volume: 38
  start-page: 367
  year: 2002
  publication-title: Comput. Stat. Data Anal.
– volume: 7
  start-page: 9
  year: 2017
  publication-title: Theory Appl. Math. Comput. Sci.
– volume: 3
  start-page: 284
  year: 2023
  publication-title: ACS Polym. Au
– year: 1993
– volume: 9
  start-page: 90
  year: 2023
  publication-title: npj Comput. Mater.
– volume: 144
  year: 2021
  publication-title: Mater. Sci. Eng., R
– volume: 71
  start-page: 456
  year: 2017
  publication-title: Appl. Spectrosc.
– volume: 10
  start-page: 66
  year: 2009
  publication-title: J. Mach. Learn. Res.
– volume: 4
  start-page: 7
  year: 2012
  publication-title: Int. J. Adv. Struct. Eng.
– volume: 129
  start-page: 3297
  year: 2013
  publication-title: J. Appl. Polym. Sci.
– volume: 101
  start-page: 2167
  year: 2006
  publication-title: J. Appl. Polym. Sci.
– volume: 8
  start-page: 540
  year: 2019
  publication-title: ACS Macro Lett.
– volume: 22
  start-page: 1345
  year: 2010
  publication-title: IEEE Trans. Knowl. Data Eng.
– volume: 57
  start-page: 115
  year: 2020
  publication-title: Inf. Fusion
– volume: 14
  start-page: 9641
  year: 2024
  publication-title: Sci. Rep.
– volume: 236
  year: 2020
  publication-title: Compos. Struct.
– volume: 3
  start-page: 353
  year: 2021
  publication-title: InfoMat
– year: 2016
– volume: 38
  start-page: 2553
  year: 2021
  publication-title: Eng. Comput.
– volume: 143
  year: 2021
  publication-title: J. Am. Chem. Soc.
– volume: 66
  start-page: 246
  year: 2015
  publication-title: Int. Commun. Heat Mass Transfer
– volume: 57
  start-page: 3515
  year: 2024
  publication-title: Macromolecules
– volume: 7
  year: 2022
  publication-title: ACS Omega
– volume: 21
  start-page: 1
  year: 2020
– volume: 37
  year: 2018
  publication-title: Mol. Inf.
– volume: 27
  start-page: 7663
  year: 2011
  publication-title: Langmuir
– start-page: 1112
  year: 2014
  end-page: 1117
– volume: 269
  year: 2024
  publication-title: Composites, Part B
– start-page: 22
  year: 2011
  end-page: 29
– volume: 109
  start-page: 43
  year: 2021
  publication-title: Proc. IEEE
– volume: 1
  year: 2006
– start-page: 105
  year: 2013
  end-page: 116
– volume: 28
  start-page: 363
  year: 2010
  publication-title: Trends Biotechnol.
– volume: 28
  start-page: 41
  year: 1997
  publication-title: Mach. Learn.
– volume: 14
  start-page: 35
  year: 2023
  publication-title: Nat. Commun.
– volume: 15
  start-page: 1405
  year: 2022
  publication-title: Pharmaceuticals
– volume: 15
  year: 2023
  publication-title: ACS Appl. Mater. Inter.
– volume: 4
  start-page: 268
  year: 2018
  publication-title: ACS Cent. Sci.
– volume: 4
  start-page: 275
  year: 2012
  publication-title: WIREs Comput Stat
– volume: 1
  year: 2020
  publication-title: Mach. Learn.: Sci. Technol.
– volume: 7
  start-page: 953
  year: 2008
  publication-title: J. Theor. Comput. Chem.
– volume: 214
  year: 2021
  publication-title: Polymer
– start-page: 397
  year: 2020
  end-page: 412
– volume: 171
  start-page: 1
  year: 2021
  publication-title: Adv. Drug Delivery Rev
– volume: 2
  start-page: 1
  year: 2009
  publication-title: ASQ Higher Education Brief
– year: 2018
– volume: 34
  year: 2022
  publication-title: Adv. Mater.
– volume: 9
  start-page: 272
  year: 2012
  publication-title: Int. J. Comput. Sci.
– volume: 2
  start-page: 200
  year: 2020
  publication-title: Nat Mach Intell
– volume: 83
  start-page: 236
  year: 2016
  publication-title: Prog. Mater. Sci.
– volume: 5
  start-page: 107
  year: 1965
  publication-title: J. Chem. Doc.
– volume: 3
  start-page: 1525
  year: 2021
  publication-title: ACS Appl. Polym. Mater.
– volume: 12
  start-page: 163
  year: 2020
  publication-title: Polymers
– volume: 26
  start-page: 1383
  year: 1981
  publication-title: J. Appl. Polym. Sci.
– volume: 24
  year: 2021
  publication-title: iScience
– volume: 14
  start-page: 26
  year: 2021
  publication-title: Polymers
– volume: 21
  start-page: 5
  year: 2006
– volume: 5
  start-page: 1394
  year: 2020
  publication-title: Nanoscale Horiz.
– volume: 63
  start-page: 3288
  year: 2023
  publication-title: J. Chem. Inf. Model.
– volume: 1
  start-page: 492
  year: 2009
  publication-title: Wiley Interdiscip. Rev.: Nanomed. Nanobiotechnol.
– volume: 23
  year: 2022
  publication-title: Int. J. Mol. Sci.
– volume: 9
  start-page: 1086
  year: 2020
  publication-title: ACS Macro Lett.
– volume: 3
  start-page: 422
  year: 2021
  publication-title: Nat. Rev. Phys.
– volume: 2
  year: 2019
  publication-title: Adv. Theory Simul.
– volume: 38
  start-page: 2567
  year: 1997
  publication-title: Polymer
– volume: 52
  start-page: 755
  year: 2019
  publication-title: Macromolecules
– volume: 2
  start-page: 78
  year: 2020
  publication-title: Front. Data Comput.
– volume: 9
  year: 2019
  publication-title: Adv. Energy Mater.
– volume: 80
  start-page: 268
  year: 2016
  publication-title: Eur. Polym. J.
– volume: 9
  start-page: 330
  year: 2023
  publication-title: ACS Cent. Sci.
– volume: 128
  year: 2021
  publication-title: Comput. Biol. Med.
– start-page: 1
  year: 2019
  end-page: 7
– year: 2006
– volume: 25
  start-page: 6495
  year: 2015
  publication-title: Adv. Funct. Mater.
– volume: 13
  year: 2000
  publication-title: Adv. Neural Inf. Process Syst.
– volume: 5
  start-page: 66
  year: 2019
  publication-title: npj Comput. Mater.
– volume: 1
  year: 2023
  publication-title: APL Mach. Learn.
– year: 2017
– volume: 113
  year: 2016
  publication-title: Proc. Natl. Acad. Sci. USA
– volume: 13
  year: 2021
  publication-title: ACS Appl. Mater. Interfaces
– volume: 9
  start-page: 806
  year: 2019
  publication-title: MRS Commun.
– volume: 60
  year: 2021
  publication-title: Angew. Chem., Int. Ed.
– volume: 57
  start-page: 933
  year: 2018
  publication-title: Angew. Chem., Int. Ed.
– volume: 49
  start-page: 1225
  year: 1996
  publication-title: J. Clin. Epidemiol.
– volume: 43
  start-page: W612
  year: 2015
  publication-title: Nucleic Acids Res.
– volume: 20
  start-page: 1010
  year: 2019
  publication-title: Sci. Technol. Adv. Mater.
– volume: 15
  start-page: 495
  year: 2023
  publication-title: Pharmaceutics
– volume: 224
  year: 2023
  publication-title: Macromol. Chem. Phys.
– volume: 11
  year: 2016
  publication-title: PLoS One
– volume: 32
  start-page: 4
  year: 2021
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– volume: 13
  start-page: 27
  year: 1994
  publication-title: IEEE Potentials
– volume: 14
  start-page: 357
  year: 2022
  publication-title: Polymers
– volume: 54
  start-page: 849
  year: 2021
  publication-title: Acc. Chem. Res.
– volume: 29
  start-page: 9436
  year: 2017
  publication-title: Chem. Mater.
– volume: 216
  year: 2023
  publication-title: Comput. Mater. Sci.
– volume: 27
  year: 2019
  publication-title: Modelling Simul. Mater. Sci. Eng.
– volume: 122
  year: 2018
  publication-title: J. Phys. Chem. C
– year: 2008
– volume: 21
  start-page: 1610
  year: 2021
  publication-title: Spine J.
– volume: 47
  start-page: 8541
  year: 2014
  publication-title: Macromolecules
– volume: 13
  year: 2023
  publication-title: Appl. Sci.
– volume: 29
  start-page: 470
  year: 2009
  publication-title: Mater. Sci. Eng. C
– year: 2019
– volume: 29
  start-page: 31
  year: 1996
  publication-title: Computer
– volume: 231
  year: 2023
  publication-title: Compos. Sci. Technol.
– volume: 14
  start-page: 1622
  year: 2023
  publication-title: Polym. Chem.
– volume: 14
  start-page: 4099
  year: 2023
  publication-title: Nat. Commun.
– volume: 48
  start-page: 130
  year: 2019
  publication-title: Chem. Lett.
– volume: 33
  start-page: 3621
  year: 2020
  publication-title: Neural Comput. Appl.
– volume: 94
  start-page: 637
  year: 2022
  publication-title: Pure Appl. Chem.
– volume: 3
  start-page: 133
  year: 2021
  publication-title: Trends Chem
– volume: 6
  start-page: 61
  year: 2020
  publication-title: npj Comput. Mater.
– year: 2007
– volume: 21
  start-page: 1263
  publication-title: IEEE Trans. Knowl. Data Eng. 2009
– volume: 9
  start-page: 102
  year: 2022
  publication-title: J. Big Data
– volume: 267
  year: 2023
  publication-title: Chem. Eng. Sci.
– volume: 9
  year: 2022
  publication-title: Adv. Sci.
– volume: 5
  year: 1983
– volume: 196
  year: 2021
  publication-title: Comput. Mater. Sci.
– volume: 149
  start-page: 4747
  year: 2024
  publication-title: Analyst
– volume: 33
  start-page: 2669
  year: 2021
  publication-title: IEEE Trans. Knowl. Data Eng.
– volume: 130
  year: 2024
  publication-title: Eng. Appl. Artif. Intell.
– year: 2022, preprint
  publication-title: ChemRxiv
– volume: 55
  start-page: 191
  year: 2017
  publication-title: J. Polym. Sci., Part A: Polym. Chem.
– volume: 11
  start-page: 6343
  year: 2021
  publication-title: Sci. Rep.
– volume: 128
  year: 2020
  publication-title: J. Appl. Phys.
– volume: 5
  start-page: 1717
  year: 2019
  publication-title: ACS Cent. Sci.
– volume: 45
  year: 2020
  publication-title: Int. J. Hydrogen Energy
– volume: 261
  start-page: 149
  year: 2017
  publication-title: J. Biotechnol.
– volume: 50
  start-page: 0
  year: 2006
– volume: 47
  start-page: 31
  year: 2017
  publication-title: J. Insur. Med.
– volume: 35
  start-page: 12
  year: 2021
  publication-title: Proc. of the AAAI Conf. on Artificial Intelligence
– year: 2009
– volume: 12
  start-page: 2825
  year: 2011
  publication-title: J. Mach. Learn. Res.
– volume: 355
  start-page: 842
  year: 2015
  publication-title: Appl. Surf. Sci.
– volume: 13
  start-page: 22
  year: 2021
  publication-title: J. Cheminf.
– volume: 42
  start-page: 232
  year: 2002
  publication-title: J. Chem. Inf. Comput.
– volume: 4
  start-page: 588
  year: 2017
  publication-title: IEEE/CAA J. Autom. Sin.
– volume: 12
  start-page: 51
  year: 2020
  publication-title: J. Cheminf.
– year: 2021
– volume: 66
  start-page: 11
  year: 2004
  publication-title: Psychosom. Med.
– volume: 3
  start-page: 239
  year: 2023
  publication-title: ACS Polym. Au
– volume: 136
  start-page: 5508
  year: 2014
  publication-title: J. Am. Chem. Soc.
– volume: 23
  start-page: 40
  year: 2022
  publication-title: Nat. Rev. Mol. Cell Biol.
– volume: 1
  start-page: 857
  year: 2017
  publication-title: Joule
– volume: 165
  start-page: 179
  year: 2018
  publication-title: Optik
– volume: 85
  start-page: 1
  year: 2017
  publication-title: J. Math. Psychol.
– volume: 99
  start-page: 117
  year: 2008
  publication-title: Biotechnol. Bioeng.
– volume: 14
  start-page: 69
  year: 2022
  publication-title: NPG Asia Mater.
– volume: 204
  start-page: 774
  year: 2023
  publication-title: Renewable Energy
– volume: 9
  start-page: 42
  year: 2023
  publication-title: npj Comput. Mater.
– volume: 58
  start-page: 1194
  year: 2018
  publication-title: J. Chem. Inf. Model.
– volume: 193
  year: 2020
  publication-title: Polymer
– volume: 3
  year: 2022
  publication-title: Cell Rep. Phys. Sci.
– volume: 332
  start-page: 115
  year: 2007
  publication-title: Chem. Phys.
– volume: 28
  start-page: 779
  year: 1996
  publication-title: Adv Neural Inform Process Syst.
– volume: 12
  start-page: 1475
  year: 2019
  publication-title: Materials
– volume: 6
  start-page: 11
  year: 2019
  publication-title: Comput Soc Netw
– volume: 177
  year: 2023
  publication-title: Comput. Chem. Eng.
– volume: 41
  start-page: 2034
  year: 2002
  publication-title: Angew. Chem., Int. Ed.
– volume: 3
  start-page: 620
  year: 2020
  publication-title: ACS Appl. Polym. Mater.
– volume: 9
  start-page: 15
  year: 2013
  publication-title: Tutorials Quant. Methods Psychol.
– volume: 6
  start-page: 105
  year: 2018
  publication-title: J. Immunother. Cancer
– volume: 57
  start-page: 1944
  year: 2022
  publication-title: Mater. Today: Proc.
– volume: 1
  start-page: 519
  year: 2022
  publication-title: Digital Discovery
– volume: 118
  year: 2021
  publication-title: Appl. Phys. Lett.
– year: 2020
– volume: 19
  start-page: 8907
  year: 2009
  publication-title: J. Mater. Chem.
– volume: 20
  start-page: 5652
  year: 2024
  publication-title: Soft Matter
– volume: 183
  year: 2022
  publication-title: Adv. Drug Delivery Rev.
– volume: 10
  start-page: 4
  year: 2018
  publication-title: J. Cheminf.
– volume: 1
  start-page: 93
  year: 2009
  publication-title: WIREs Comput Stat
– volume: 10
  start-page: 306
  year: 2017
  publication-title: Energy Environ. Sci.
– volume: 136
  start-page: 142
  year: 2018
  publication-title: Stat. Probab. Lett.
– volume: 7
  start-page: 510
  year: 2023
  publication-title: ACS Appl. Bio Mater.
– volume: 60
  start-page: 22
  year: 2019
  publication-title: J. Chem. Inf. Model.
– start-page: 1
  year: 2021
  end-page: 8
– volume: 291
  year: 2024
  publication-title: Chem. Eng. Sci.
– volume: 8
  year: 2020
  publication-title: IEEE Access
– volume: 28
  year: 2015
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 1
  start-page: 134
  year: 2021
  publication-title: ACS Polym. Au
– volume: 7
  start-page: 8939
  year: 2024
  publication-title: ACS Appl. Nano Mater.
– start-page: 1
  year: 2008
  end-page: 50
– volume: 792
  year: 1990
– volume: 4
  year: 1995
– volume: 143
  start-page: 1
  year: 2022
  publication-title: Acta Biomater.
– volume: 14
  year: 2020
  publication-title: ACS Nano
– volume: 8
  start-page: 2
  year: 1969
  publication-title: Ind. Eng. Chem. Prod. Res. Dev.
– volume: 146
  start-page: 265
  year: 2019
  publication-title: Carbon
– year: 2022
– volume: 63
  start-page: 36
  year: 2006
  publication-title: Cell. Mol. Life Sci.
– volume: 140
  start-page: 86
  year: 2015
  publication-title: Chemom. Intell. Lab. Syst.
– volume: 55
  start-page: 2691
  year: 2022
  publication-title: Macromolecules
– volume: 230
  year: 2023
  publication-title: Comput. Mater. Sci.
– volume: 34
  year: 2024
  publication-title: Adv. Funct. Mater.
– volume: 60
  year: 2021
  publication-title: Ind. Eng. Chem. Res.
– volume: 20
  year: 2018
  publication-title: Plasma Sci. Technol.
– start-page: 2490
  year: 2021
  end-page: 2497
– volume: 3
  start-page: 1
  year: 2011
  publication-title: J. Cheminf.
– volume: 13
  start-page: 6108
  year: 2022
  publication-title: Polym. Chem.
– volume: 4
  start-page: 25
  year: 2018
  publication-title: npj Comput. Mater.
– volume: 14
  start-page: 3325
  year: 2023
  publication-title: Polym. Chem.
– start-page: 232
  end-page: 238
– volume: 14
  start-page: 82
  year: 2022
  publication-title: Polymers
– volume: 10
  start-page: 139
  year: 2024
  publication-title: npj Comput. Mater.
– volume: 8
  start-page: 321
  year: 1931
  publication-title: Chem. Rev.
– volume: 58
  start-page: 267
  year: 1996
  publication-title: J. R. Stat.
– volume: 11
  start-page: 1250
  year: 2019
  publication-title: Polymers
– volume: 12
  start-page: 7946
  year: 2018
  publication-title: ACS Nano
– volume: 52
  start-page: 2796
  year: 2012
  publication-title: J. Chem. Inf. Model.
– volume: 19
  start-page: 303
  year: 1993
  publication-title: Comput. Geosci.
– year: 2010
– volume: 134
  year: 2017
  publication-title: J. Appl. Polym. Sci.
– volume: 114
  year: 2015
  publication-title: Phys. Rev. Lett.
– volume: 7
  start-page: 661
  year: 2022
  publication-title: Mol. Syst. Des. Eng.
– volume: 5
  year: 2022
  publication-title: Adv. Theory Simul.
– volume: 2
  start-page: 213
  year: 2022
  publication-title: ACS Polym. Au
– volume: 5
  start-page: 1689
  year: 2020
  publication-title: ACS Sens.
– volume: 34
  start-page: 99
  year: 2005
  publication-title: Comput. Mater. Sci.
– start-page: 3304
  year: 2012
  end-page: 3308
– volume: 29
  start-page: 20
  year: 2007
  publication-title: Int. J. Fatigue
– volume: 31
  year: 2021
  publication-title: Adv. Funct. Mater.
– volume: 17
  start-page: 1365
  year: 2022
  publication-title: Int. J. Nanomed.
– volume: 61
  start-page: 5377
  year: 2021
  publication-title: J. Chem. Inf. Model.
– volume: 304
  year: 2021
  publication-title: Mater. Lett.
– volume: 15
  year: 2023
  publication-title: Giant
– volume: 32
  year: 2020
  publication-title: Chem. Mater.
– volume: 9
  start-page: 860
  year: 2019
  publication-title: MRS Commun.
– volume: 10
  start-page: 1339
  year: 2021
  publication-title: ACS Macro Lett.
– volume: 9
  start-page: 556
  year: 2019
  publication-title: MRS Commun.
– volume: 146
  start-page: 8120
  year: 2024
  publication-title: J. Am. Chem. Soc.
– volume: 45
  start-page: 5
  year: 2001
  publication-title: Mach. Learn.
– volume: 5
  start-page: 1523
  year: 2019
  publication-title: ACS Cent. Sci
– volume: 57
  start-page: 6727
  year: 2024
  publication-title: Macromolecules
– year: 2001
– volume: 11
  start-page: 1486
  year: 2020
  publication-title: Nat. Commun.
– volume: 20
  start-page: 2908
  year: 2024
  publication-title: J. Chem. Theory Comput.
– volume: 124
  start-page: 3873
  year: 2020
  publication-title: J. Phys. Chem. B
– volume: 35
  start-page: 53
  year: 2018
  publication-title: IEEE Signal Process Mag.
– volume: 712
  year: 2024
  publication-title: J. Membr. Sci.
– volume: 30
  start-page: 595
  year: 2016
  publication-title: J. Comput.‐Aided Mol. Des.
– volume: 66
  start-page: 411
  year: 2004
  publication-title: Psychosom. Med.
– volume: 11
  start-page: 1656
  year: 2021
  publication-title: Nanomaterials
– volume: 2
  start-page: 1
  year: 2015
  publication-title: J. Big Data
– volume: 187
  year: 2021
  publication-title: Comput. Mater. Sci.
– volume: 46
  start-page: 151
  year: 1942
  publication-title: J. Phys. Chem.
– volume: 14
  start-page: 106
  year: 2013
  publication-title: BMC Bioinf.
– volume: 44
  start-page: 37
  year: 1949
  publication-title: Chem. Rev.
– volume: 14
  start-page: 1802
  year: 2022
  publication-title: Polymers
– volume: 3
  start-page: 54
  year: 2017
  publication-title: npj Comput. Mater.
– volume: 16
  start-page: 1049
  year: 2024
  publication-title: Polymers
– volume: 5
  start-page: 562
  year: 2020
  publication-title: Nat. Rev. Mater.
– volume: 6
  year: 2020
  publication-title: Heliyon
– volume: 47
  start-page: D930
  year: 2019
  publication-title: Nucleic Acids Res.
– volume: 14
  start-page: 2779
  year: 2023
  publication-title: Polym. Chem.
– volume: 45
  start-page: 257
  year: 2009
  publication-title: Comput. Mater. Sci.
– volume: 36
  start-page: 2602
  year: 2024
  publication-title: Chem. Mater.
– volume: 5
  start-page: 9784
  year: 2019
  publication-title: Sci. Adv.
– volume: 6
  start-page: 642
  year: 2021
  publication-title: Nat. Rev. Mater.
– volume: 69
  start-page: 337
  year: 2012
  publication-title: Cell. Mol. Life Sci.
– volume: 14
  start-page: 2383
  year: 2023
  publication-title: Polym. Chem.
– volume: 9
  year: 2019
  publication-title: Sci. Rep.
– volume: 43
  start-page: 514
  year: 2020
  publication-title: Chem. Eng. Technol.
– volume: 27
  start-page: 177
  year: 1958
  publication-title: J. Polym. Sci.
– volume: 65
  year: 2002
  publication-title: Phys. Rev. E
– year: 2012
– year: 2023， 347
  publication-title: Mater. Today: Proc.
– volume: 188
  year: 2021
  publication-title: Comput. Mater. Sci.
– volume: 220
  year: 2022
  publication-title: Compos. Sci. Technol.
– volume: 7
  start-page: 5683
  year: 2017
  publication-title: Sci. Rep.
– volume: 13
  year: 2021
  publication-title: ACS Appl. Mater. Inter.
– volume: 260
  start-page: 29
  year: 2005
  publication-title: Colloids Surf., A
– volume: 10
  start-page: 51
  year: 1942
  publication-title: J. Chem. Phys.
– volume: 28
  start-page: 144
  year: 2020
  publication-title: J. Sustainable Tourism
– volume: 54
  start-page: 3396
  year: 2014
  publication-title: J. Chem. Inf. Model.
– year: 2023
– volume: 12
  start-page: 191
  year: 2013
  publication-title: Nat. Mater.
– volume: 6
  start-page: 6216
  year: 2020
  publication-title: Sci. Adv.
– volume: 16
  start-page: 115
  year: 2023
  publication-title: Polymers
– volume: 68
  start-page: 30
  year: 2020
  publication-title: IJETT
– volume: 5
  start-page: 962
  year: 2020
  publication-title: Mol. Syst. Des. Eng.
– volume: 51
  start-page: 77
  year: 2004
  publication-title: Prog. Org. Coat.
– volume: 12
  start-page: 1002
  year: 2021
  publication-title: Polym. Chem.
– volume: 21
  start-page: 3356
  year: 2024
  publication-title: Mol. Pharmaceutics
– volume: 1
  start-page: 12
  year: 2021
  publication-title: Sci. Technol. Adv. Mater.: Methods
– volume: 194
  year: 2021
  publication-title: Comput. Mater. Sci.
– volume: 56
  start-page: 5446
  year: 2023
  publication-title: Macromolecules
– volume: 34
  start-page: 13
  year: 1963
  publication-title: J. Appl. Phys.
– volume: 5
  start-page: 83
  year: 2019
  publication-title: npj Comput. Mater.
– volume: 220
  year: 2021
  publication-title: Polymer
– ident: e_1_2_8_265_1
  doi: 10.1016/j.patrec.2017.03.008
– ident: e_1_2_8_189_1
  doi: 10.3934/mbe.2024061
– ident: e_1_2_8_386_1
  doi: 10.1002/(SICI)1521-3773(19981102)37:20<2754::AID-ANIE2754>3.0.CO;2-3
– ident: e_1_2_8_118_1
  doi: 10.1109/ICADIWT.2014.6814687
– ident: e_1_2_8_156_1
– ident: e_1_2_8_113_1
  doi: 10.1021/ci010062o
– ident: e_1_2_8_94_1
  doi: 10.1021/ci100050t
– ident: e_1_2_8_299_1
  doi: 10.1016/j.icheatmasstransfer.2015.06.002
– ident: e_1_2_8_225_1
  doi: 10.1186/s40649-019-0069-y
– ident: e_1_2_8_358_1
  doi: 10.1016/j.pmatsci.2024.101282
– ident: e_1_2_8_319_1
  doi: 10.1016/j.ces.2024.119952
– ident: e_1_2_8_54_1
  doi: 10.1088/1674-1056/27/11/118101
– ident: e_1_2_8_186_1
  doi: 10.1002/ceat.201900096
– volume-title: Proceedings of the eighteenth annual ACM‐SIAM symposium on Discrete algorithms
  year: 2007
  ident: e_1_2_8_343_1
– ident: e_1_2_8_63_1
  doi: 10.1186/s40537-016-0043-6
– ident: e_1_2_8_347_1
  doi: 10.1016/j.apsadv.2021.100068
– ident: e_1_2_8_379_1
  doi: 10.1002/pola.28378
– ident: e_1_2_8_269_1
– ident: e_1_2_8_139_1
  doi: 10.1002/bit.21531
– ident: e_1_2_8_138_1
  doi: 10.1007/s00396-009-2035-y
– volume-title: Essential Math for Data Science
  year: 2022
  ident: e_1_2_8_114_1
– ident: e_1_2_8_144_1
  doi: 10.1016/j.chemolab.2012.10.003
– ident: e_1_2_8_288_1
  doi: 10.1016/j.polymer.2020.122341
– ident: e_1_2_8_377_1
  doi: 10.1002/ange.201600112
– ident: e_1_2_8_43_1
  doi: 10.1016/j.carbon.2019.02.001
– ident: e_1_2_8_137_1
  doi: 10.1007/s00396-012-2743-6
– ident: e_1_2_8_325_1
  doi: 10.1039/C5MH00282F
– ident: e_1_2_8_297_1
  doi: 10.1016/j.polymer.2021.123558
– ident: e_1_2_8_15_1
  doi: 10.1063/1.474002
– ident: e_1_2_8_163_1
  doi: 10.1023/B:STCO.0000035301.49549.88
– ident: e_1_2_8_365_1
  doi: 10.1186/s40425-018-0416-9
– ident: e_1_2_8_146_1
  doi: 10.1007/978-3-642-41136-6_11
– ident: e_1_2_8_436_1
  doi: 10.1109/TKDE.2017.2720168
– volume: 68
  start-page: 30
  year: 2020
  ident: e_1_2_8_444_1
  publication-title: IJETT
– ident: e_1_2_8_286_1
  doi: 10.1016/j.heliyon.2020.e05055
– ident: e_1_2_8_52_1
  doi: 10.1038/s41524-023-01000-z
– ident: e_1_2_8_422_1
  doi: 10.1021/acs.jcim.1c00554
– start-page: 3304
  volume-title: ICPR2012
  year: 2012
  ident: e_1_2_8_177_1
– ident: e_1_2_8_194_1
  doi: 10.20982/tqmp.09.1.p015
– ident: e_1_2_8_423_1
  doi: 10.1007/978-3-030-40245-7_18
– ident: e_1_2_8_28_1
  doi: 10.1002/pi.6345
– ident: e_1_2_8_382_1
  doi: 10.1039/C7SM02472J
– ident: e_1_2_8_222_1
  doi: 10.1007/s10822-016-9938-8
– ident: e_1_2_8_89_1
  doi: 10.1126/sciadv.abc6216
– ident: e_1_2_8_230_1
  doi: 10.1246/bcsj.20210253
– ident: e_1_2_8_352_1
  doi: 10.3390/polym14010082
– ident: e_1_2_8_404_1
  doi: 10.1021/acs.molpharmaceut.3c00880
– ident: e_1_2_8_305_1
  doi: 10.1016/j.compositesb.2023.111099
– ident: e_1_2_8_80_1
  doi: 10.1002/marc.202100400
– ident: e_1_2_8_315_1
  doi: 10.1063/1.1723621
– ident: e_1_2_8_153_1
  doi: 10.1109/TKDE.2019.2953728
– ident: e_1_2_8_187_1
  doi: 10.1016/S0167-9473(01)00065-2
– ident: e_1_2_8_185_1
  doi: 10.1002/app.23112
– ident: e_1_2_8_448_1
  doi: 10.1109/MIPRO.2014.6859735
– ident: e_1_2_8_268_1
  doi: 10.1016/j.crfs.2021.01.002
– ident: e_1_2_8_7_1
  doi: 10.1038/s41598-017-05723-0
– ident: e_1_2_8_106_1
  doi: 10.1038/s41467-023-39868-6
– ident: e_1_2_8_149_1
  doi: 10.1016/j.inffus.2020.07.007
– ident: e_1_2_8_267_1
  doi: 10.1016/j.msec.2008.08.041
– ident: e_1_2_8_241_1
  doi: 10.1080/14686996.2019.1673670
– ident: e_1_2_8_198_1
  doi: 10.1038/s41580-021-00407-0
– ident: e_1_2_8_77_1
  doi: 10.1186/s13321-019-0397-9
– ident: e_1_2_8_369_1
  doi: 10.1145/2939672.2939785
– ident: e_1_2_8_418_1
  doi: 10.1016/j.isci.2020.101922
– ident: e_1_2_8_317_1
  doi: 10.1039/D3PY01028G
– ident: e_1_2_8_431_1
  doi: 10.1038/s42254-021-00314-5
– ident: e_1_2_8_167_1
  doi: 10.3390/ph15111405
– ident: e_1_2_8_204_1
– volume-title: Categorical Data Analysis
  year: 1990
  ident: e_1_2_8_115_1
– volume-title: Recurrent Neural Networks
  year: 2001
  ident: e_1_2_8_173_1
– ident: e_1_2_8_424_1
  doi: 10.1021/acsmacrolett.0c00264
– ident: e_1_2_8_73_1
  doi: 10.1021/acs.chemmater.0c03332
– volume: 4
  start-page: 139
  year: 2016
  ident: e_1_2_8_323_1
  publication-title: Int. J. Comput. Sci. Eng.
– ident: e_1_2_8_123_1
  doi: 10.1063/1.5017661
– ident: e_1_2_8_332_1
  doi: 10.1021/i260029a002
– ident: e_1_2_8_32_1
  doi: 10.1021/acsmacrolett.7b00228
– ident: e_1_2_8_174_1
  doi: 10.1016/j.physd.2019.132306
– ident: e_1_2_8_233_1
– ident: e_1_2_8_443_1
  doi: 10.1016/j.matpr.2022.03.051
– ident: e_1_2_8_33_1
  doi: 10.1021/acs.accounts.0c00785
– ident: e_1_2_8_109_1
  doi: 10.1186/1758-2946-3-1
– ident: e_1_2_8_273_1
  doi: 10.3390/polym12010163
– volume: 6
  year: 2021
  ident: e_1_2_8_130_1
  publication-title: Mach. Learn. Appl.
– volume: 75
  start-page: 125
  year: 2008
  ident: e_1_2_8_235_1
  publication-title: J. Dent. Child.
– ident: e_1_2_8_12_1
  doi: 10.1002/adts.202100565
– ident: e_1_2_8_11_1
  doi: 10.1002/aisy.202200243
– ident: e_1_2_8_121_1
  doi: 10.1016/j.jpdc.2019.04.008
– ident: e_1_2_8_321_1
  doi: 10.1021/acs.chemmater.3c02358
– ident: e_1_2_8_208_1
  doi: 10.1021/acsami.1c20947
– ident: e_1_2_8_51_1
  doi: 10.1002/adts.202100565
– ident: e_1_2_8_59_1
  doi: 10.1038/npjcompumats.2016.31
– ident: e_1_2_8_326_1
  doi: 10.1016/j.joule.2017.10.006
– ident: e_1_2_8_76_1
  doi: 10.1016/j.commatsci.2021.110278
– ident: e_1_2_8_70_1
  doi: 10.1103/PhysRevLett.114.105503
– ident: e_1_2_8_282_1
  doi: 10.1039/D3PY00246B
– ident: e_1_2_8_274_1
  doi: 10.3390/app132111991
– ident: e_1_2_8_219_1
– ident: e_1_2_8_148_1
  doi: 10.1016/j.apmt.2021.101158
– ident: e_1_2_8_19_1
  doi: 10.1021/acspolymersau.2c00053
– ident: e_1_2_8_383_1
  doi: 10.1021/acsapm.0c01376
– ident: e_1_2_8_348_1
  doi: 10.1002/1521-3773(20020617)41:12<2034::AID-ANIE2034>3.0.CO;2-M
– ident: e_1_2_8_387_1
  doi: 10.1021/acs.bioconjchem.8b00804
– volume: 2
  start-page: 1
  year: 2009
  ident: e_1_2_8_159_1
  publication-title: ASQ Higher Education Brief
– volume: 13
  year: 2000
  ident: e_1_2_8_145_1
  publication-title: Adv. Neural Inf. Process Syst.
– ident: e_1_2_8_345_1
  doi: 10.1021/acs.macromol.2c00245
– ident: e_1_2_8_30_1
  doi: 10.1039/D1ME00160D
– ident: e_1_2_8_255_1
  doi: 10.1246/cl.180847
– volume: 12
  start-page: 2825
  year: 2011
  ident: e_1_2_8_371_1
  publication-title: J. Mach. Learn. Res.
– ident: e_1_2_8_341_1
  doi: 10.1016/S0032-3861(97)01024-0
– ident: e_1_2_8_395_1
  doi: 10.3390/nano11071656
– ident: e_1_2_8_280_1
  doi: 10.1016/j.compchemeng.2023.108356
– ident: e_1_2_8_93_1
  doi: 10.1021/acscentsci.9b00476
– ident: e_1_2_8_328_1
  doi: 10.1021/cr60030a010
– ident: e_1_2_8_182_1
  doi: 10.1016/S0263-8223(00)00179-3
– ident: e_1_2_8_246_1
  doi: 10.1016/j.commatsci.2008.09.027
– ident: e_1_2_8_338_1
  doi: 10.1021/acs.macromol.2c02600
– ident: e_1_2_8_237_1
  doi: 10.1016/j.polymdegradstab.2004.03.006
– ident: e_1_2_8_271_1
  doi: 10.1021/jacsau.3c00275
– ident: e_1_2_8_261_1
  doi: 10.1002/app.45237
– ident: e_1_2_8_110_1
  doi: 10.1088/2632-2153/aba947
– ident: e_1_2_8_112_1
  doi: 10.1021/acs.jpcc.8b02913
– ident: e_1_2_8_239_1
– ident: e_1_2_8_41_1
  doi: 10.1186/s13321-021-00502-6
– ident: e_1_2_8_45_1
  doi: 10.1093/nar/gkaa1088
– ident: e_1_2_8_256_1
  doi: 10.1021/acs.jpclett.1c03526
– volume: 28
  start-page: 779
  year: 1996
  ident: e_1_2_8_160_1
  publication-title: Adv Neural Inform Process Syst.
– ident: e_1_2_8_442_1
  doi: 10.1557/mrc.2019.90
– ident: e_1_2_8_78_1
  doi: 10.1021/acs.nanolett.1c04469
– ident: e_1_2_8_31_1
  doi: 10.1038/s41524-017-0056-5
– ident: e_1_2_8_279_1
  doi: 10.1038/s41467-023-40459-8
– ident: e_1_2_8_370_1
– ident: e_1_2_8_425_1
  doi: 10.1021/acs.macromol.2c02249
– ident: e_1_2_8_353_1
  doi: 10.1016/j.polymer.2020.123351
– ident: e_1_2_8_228_1
  doi: 10.1021/acs.chemmater.2c02991
– ident: e_1_2_8_20_1
  doi: 10.1038/s41586-021-03819-2
– ident: e_1_2_8_232_1
  doi: 10.1002/wics.1198
– ident: e_1_2_8_108_1
  doi: 10.1021/ci3001925
– ident: e_1_2_8_429_1
– volume: 10
  start-page: 66
  year: 2009
  ident: e_1_2_8_203_1
  publication-title: J. Mach. Learn. Res.
– ident: e_1_2_8_24_1
  doi: 10.1039/D3PY00314K
– ident: e_1_2_8_190_1
  doi: 10.1023/A:1010933404324
– ident: e_1_2_8_119_1
  doi: 10.1103/PhysRevE.105.035304
– ident: e_1_2_8_253_1
  doi: 10.1007/s00366-020-01226-1
– ident: e_1_2_8_428_1
  doi: 10.1007/s00018-005-5350-6
– ident: e_1_2_8_221_1
– ident: e_1_2_8_294_1
– ident: e_1_2_8_401_1
  doi: 10.1021/acsabm.2c00962
– ident: e_1_2_8_375_1
  doi: 10.1021/mz4003744
– volume: 28
  year: 2015
  ident: e_1_2_8_223_1
  publication-title: Adv. Neural Inf. Process. Syst.
– ident: e_1_2_8_215_1
  doi: 10.1109/TNN.2008.2005605
– ident: e_1_2_8_333_1
  doi: 10.1016/j.porgcoat.2004.05.004
– ident: e_1_2_8_133_1
– ident: e_1_2_8_434_1
  doi: 10.1103/PhysRevE.106.014503
– ident: e_1_2_8_281_1
  doi: 10.1021/acs.macromol.4c00835
– ident: e_1_2_8_320_1
  doi: 10.1021/acspolymersau.1c00027
– volume: 1
  start-page: 12
  year: 2021
  ident: e_1_2_8_419_1
  publication-title: Sci. Technol. Adv. Mater.: Methods
– ident: e_1_2_8_380_1
  doi: 10.1002/anie.202104204
– ident: e_1_2_8_162_1
  doi: 10.1007/978-1-4302-5990-9
– ident: e_1_2_8_164_1
  doi: 10.4028/www.scientific.net/AMR.455-456.436
– ident: e_1_2_8_364_1
  doi: 10.1002/wnan.35
– ident: e_1_2_8_87_1
  doi: 10.1002/pol.20210555
– ident: e_1_2_8_301_1
  doi: 10.1016/j.memsci.2024.123169
– ident: e_1_2_8_234_1
  doi: 10.1557/mrc.2019.32
– ident: e_1_2_8_400_1
  doi: 10.1371/journal.pone.0224365
– ident: e_1_2_8_316_1
  doi: 10.1021/j150415a018
– ident: e_1_2_8_388_1
  doi: 10.1039/D4SM00453A
– ident: e_1_2_8_251_1
  doi: 10.1186/s40537-014-0007-7
– start-page: 306
  year: 2017
  ident: e_1_2_8_244_1
  publication-title: Pattern Recognit.
– ident: e_1_2_8_304_1
  doi: 10.3390/ijms231810712
– ident: e_1_2_8_50_1
  doi: 10.1080/09669582.2019.1631318
– ident: e_1_2_8_195_1
  doi: 10.1088/2058-6272/aaaade
– ident: e_1_2_8_217_1
  doi: 10.1109/TNNLS.2020.2978386
– ident: e_1_2_8_339_1
  doi: 10.1002/adts.201800069
– ident: e_1_2_8_39_1
  doi: 10.1038/sdata.2016.12
– ident: e_1_2_8_22_1
  doi: 10.1016/j.ces.2022.118326
– ident: e_1_2_8_446_1
  doi: 10.1016/j.jbiotec.2017.07.028
– ident: e_1_2_8_441_1
  doi: 10.1109/TKDE.2009.191
– ident: e_1_2_8_69_1
  doi: 10.1109/ICPR48806.2021.9412492
– ident: e_1_2_8_71_1
  doi: 10.1038/npjcompumats.2016.28
– ident: e_1_2_8_245_1
  doi: 10.1007/s00521-020-05191-0
– ident: e_1_2_8_111_1
  doi: 10.1002/aisy.202070042
– ident: e_1_2_8_92_1
  doi: 10.1021/ci00057a005
– ident: e_1_2_8_257_1
  doi: 10.17849/insm-47-01-31-39.1
– volume: 30
  year: 2017
  ident: e_1_2_8_220_1
  publication-title: Adv. Neural Inf. Process. Syst.
– ident: e_1_2_8_311_1
  doi: 10.1201/b18063
– ident: e_1_2_8_335_1
  doi: 10.1021/acs.iecr.1c02142
– ident: e_1_2_8_48_1
  doi: 10.1038/sdata.2016.18
– ident: e_1_2_8_242_1
  doi: 10.1016/j.compscitech.2018.04.017
– year: 2023
  ident: e_1_2_8_362_1
  publication-title: Mater. Today: Proc.
– ident: e_1_2_8_447_1
– ident: e_1_2_8_314_1
  doi: 10.1063/1.1729054
– ident: e_1_2_8_98_1
  doi: 10.1186/s13321-020-00456-1
– ident: e_1_2_8_420_1
  doi: 10.1016/j.inffus.2019.12.001
– ident: e_1_2_8_65_1
  doi: 10.1038/s41524-019-0203-2
– ident: e_1_2_8_216_1
  doi: 10.1016/j.aiopen.2021.01.001
– volume: 66
  start-page: 11
  year: 2004
  ident: e_1_2_8_260_1
  publication-title: Psychosom. Med.
– ident: e_1_2_8_300_1
  doi: 10.1039/C6EE02697D
– ident: e_1_2_8_392_1
  doi: 10.1021/acs.jcim.3c00460
– ident: e_1_2_8_409_1
  doi: 10.1021/acs.molpharmaceut.4c00086
– ident: e_1_2_8_200_1
  doi: 10.1016/j.apsusc.2015.07.066
– ident: e_1_2_8_252_1
  doi: 10.1016/S0895-4356(96)00002-9
– ident: e_1_2_8_307_1
  doi: 10.1002/app.1981.070260429
– ident: e_1_2_8_120_1
  doi: 10.1016/B978-012691360-6/50013-6
– ident: e_1_2_8_381_1
  doi: 10.1039/D0PY01551B
– ident: e_1_2_8_84_1
  doi: 10.1021/jacs.1c08181
– ident: e_1_2_8_340_1
  doi: 10.1021/acs.jpca.3c05892
– ident: e_1_2_8_426_1
  doi: 10.1021/acscentsci.3c00011
– ident: e_1_2_8_172_1
  doi: 10.1109/45.329294
– ident: e_1_2_8_102_1
  doi: 10.1021/acs.jcim.1c01031
– ident: e_1_2_8_181_1
  doi: 10.1109/MSP.2017.2765202
– ident: e_1_2_8_440_1
– ident: e_1_2_8_433_1
  doi: 10.1021/acsmacrolett.1c00117
– ident: e_1_2_8_374_1
  doi: 10.1007/s00018-011-0852-x
– volume: 7
  start-page: 9
  year: 2017
  ident: e_1_2_8_129_1
  publication-title: Theory Appl. Math. Comput. Sci.
– ident: e_1_2_8_360_1
  doi: 10.1002/adfm.202309844
– ident: e_1_2_8_1_1
  doi: 10.1007/978-3-319-23871-5
– ident: e_1_2_8_231_1
  doi: 10.1016/j.chemolab.2014.11.008
– ident: e_1_2_8_166_1
  doi: 10.1007/s11433-011-4319-8
– ident: e_1_2_8_18_1
  doi: 10.1021/acssensors.0c00329
– ident: e_1_2_8_415_1
  doi: 10.1016/j.chempr.2022.12.003
– ident: e_1_2_8_391_1
  doi: 10.1016/j.commatsci.2021.110511
– ident: e_1_2_8_68_1
– ident: e_1_2_8_390_1
  doi: 10.1039/D1SM01012C
– ident: e_1_2_8_201_1
  doi: 10.1016/j.colsurfa.2005.02.029
– ident: e_1_2_8_209_1
– ident: e_1_2_8_3_1
  doi: 10.1002/inf2.12167
– ident: e_1_2_8_336_1
  doi: 10.1007/s10118-022-2716-2
– ident: e_1_2_8_406_1
  doi: 10.1021/acsnano.0c08549
– ident: e_1_2_8_363_1
  doi: 10.1016/j.tibtech.2010.04.002
– volume: 9
  start-page: 1
  year: 1997
  ident: e_1_2_8_205_1
  publication-title: University of Sheffield. Tech. Rep.
– ident: e_1_2_8_61_1
  doi: 10.1038/s41578-021-00282-3
– ident: e_1_2_8_57_1
  doi: 10.1021/acs.chemmater.7b03500
– ident: e_1_2_8_306_1
  doi: 10.1039/b909690f
– ident: e_1_2_8_6_1
  doi: 10.1021/acs.macromol.8b01836
– ident: e_1_2_8_175_1
– ident: e_1_2_8_393_1
  doi: 10.1021/la2015034
– ident: e_1_2_8_384_1
  doi: 10.1021/jacs.3c12502
– ident: e_1_2_8_309_1
  doi: 10.1016/j.commatsci.2021.110460
– ident: e_1_2_8_283_1
  doi: 10.1039/D2PY01531E
– ident: e_1_2_8_310_1
  doi: 10.1016/j.memsci.2008.04.030
– ident: e_1_2_8_117_1
  doi: 10.1080/08927022.2020.1851028
– ident: e_1_2_8_259_1
  doi: 10.1007/s42107-023-00721-2
– ident: e_1_2_8_122_1
  doi: 10.1016/j.acha.2006.04.006
– ident: e_1_2_8_214_1
– ident: e_1_2_8_355_1
  doi: 10.1016/j.compscitech.2022.109818
– ident: e_1_2_8_135_1
  doi: 10.1016/j.spinee.2020.10.006
– ident: e_1_2_8_191_1
  doi: 10.1021/ci060164k
– ident: e_1_2_8_313_1
  doi: 10.1002/pol.1958.1202711515
– ident: e_1_2_8_64_1
  doi: 10.1109/JPROC.2020.3004555
– ident: e_1_2_8_258_1
  doi: 10.1016/j.commatsci.2020.110108
– ident: e_1_2_8_157_1
  doi: 10.1038/s41524-020-0333-6
– ident: e_1_2_8_158_1
  doi: 10.1016/j.commatsci.2022.111859
– ident: e_1_2_8_247_1
  doi: 10.1016/j.commatsci.2004.11.001
– ident: e_1_2_8_367_1
  doi: 10.1021/cr500286d
– ident: e_1_2_8_46_1
  doi: 10.1093/nar/gkv352
– ident: e_1_2_8_329_1
  doi: 10.1021/cr60137a003
– ident: e_1_2_8_312_1
  doi: 10.3390/membranes12090857
– ident: e_1_2_8_378_1
  doi: 10.1021/acs.macromol.7b01379
– ident: e_1_2_8_303_1
  doi: 10.1016/j.compscitech.2022.109293
– ident: e_1_2_8_354_1
  doi: 10.1021/acsami.2c14543
– ident: e_1_2_8_193_1
  doi: 10.1021/acs.jpclett.8b00635
– ident: e_1_2_8_350_1
  doi: 10.1038/s41578-020-0202-4
– ident: e_1_2_8_74_1
  doi: 10.1021/acscentsci.7b00572
– ident: e_1_2_8_103_1
  doi: 10.1021/acs.jcim.9b00587
– ident: e_1_2_8_62_1
  doi: 10.1021/acscentsci.9b00804
– ident: e_1_2_8_414_1
  doi: 10.1002/adfm.202011168
– volume-title: The Three Dimensional Solubility Parameter
  year: 1967
  ident: e_1_2_8_331_1
– ident: e_1_2_8_396_1
  doi: 10.1038/s41467-020-15259-z
– ident: e_1_2_8_83_1
  doi: 10.1002/9783527628407
– ident: e_1_2_8_206_1
  doi: 10.1002/minf.201700133
– ident: e_1_2_8_285_1
  doi: 10.1016/j.chemphys.2006.11.029
– ident: e_1_2_8_82_1
  doi: 10.1021/ja501745g
– ident: e_1_2_8_292_1
  doi: 10.1021/ci5004834
– ident: e_1_2_8_435_1
  doi: 10.3389/fmats.2016.00028
– ident: e_1_2_8_58_1
  doi: 10.1039/D2DD00035K
– ident: e_1_2_8_151_1
  doi: 10.1109/MCSE.2023.3342149
– ident: e_1_2_8_147_1
  doi: 10.1063/5.0008026
– ident: e_1_2_8_171_1
– ident: e_1_2_8_134_1
– ident: e_1_2_8_179_1
– ident: e_1_2_8_318_1
  doi: 10.1021/acs.jcim.0c00259
– ident: e_1_2_8_169_1
  doi: 10.1109/2.485891
– ident: e_1_2_8_38_1
  doi: 10.1021/acs.jcim.0c00726
– ident: e_1_2_8_13_1
  doi: 10.3389/fchem.2021.820417
– ident: e_1_2_8_296_1
– ident: e_1_2_8_2_1
  doi: 10.1016/j.actbio.2022.02.027
– ident: e_1_2_8_218_1
– ident: e_1_2_8_14_1
  doi: 10.1103/PhysRevE.65.021507
– ident: e_1_2_8_289_1
  doi: 10.1016/j.xcrp.2022.100911
– ident: e_1_2_8_349_1
  doi: 10.1016/j.eurpolymj.2016.05.004
– ident: e_1_2_8_10_1
  doi: 10.1016/j.mser.2020.100595
– ident: e_1_2_8_155_1
  doi: 10.1109/IISA52424.2021.9555522
– ident: e_1_2_8_9_1
  doi: 10.1063/5.0023759
– ident: e_1_2_8_96_1
  doi: 10.1021/c160017a018
– ident: e_1_2_8_275_1
  doi: 10.1109/ICIEA.2019.8833686
– ident: e_1_2_8_357_1
  doi: 10.1038/s41598-019-53570-y
– ident: e_1_2_8_116_1
  doi: 10.1039/D0NH00382D
– ident: e_1_2_8_152_1
  doi: 10.1016/j.jmp.2018.03.001
– ident: e_1_2_8_210_1
  doi: 10.1038/s41598-024-59252-8
– ident: e_1_2_8_124_1
  doi: 10.1557/mrc.2019.78
– ident: e_1_2_8_211_1
  doi: 10.1016/j.engappai.2023.107796
– ident: e_1_2_8_266_1
  doi: 10.1016/j.compstruct.2019.111840
– ident: e_1_2_8_250_1
  doi: 10.1016/j.ijfatigue.2006.03.004
– ident: e_1_2_8_178_1
– ident: e_1_2_8_105_1
– ident: e_1_2_8_226_1
  doi: 10.1021/acsomega.2c04649
– ident: e_1_2_8_100_1
  doi: 10.1021/acsami.3c03298
– ident: e_1_2_8_213_1
  doi: 10.1021/acs.jcim.7b00690
– ident: e_1_2_8_104_1
  doi: 10.1007/978-81-322-3972-7
– ident: e_1_2_8_126_1
  doi: 10.1038/s41427-022-00416-1
– ident: e_1_2_8_97_1
  doi: 10.1186/s13321-018-0258-y
– ident: e_1_2_8_264_1
  doi: 10.1002/app.39059
– ident: e_1_2_8_85_1
  doi: 10.1002/anie.202308838
– ident: e_1_2_8_276_1
  doi: 10.1109/JAS.2017.7510583
– ident: e_1_2_8_227_1
  doi: 10.1021/acspolymersau.1c00050
– ident: e_1_2_8_427_1
  doi: 10.1515/pac-2022-0101
– ident: e_1_2_8_416_1
  doi: 10.1016/j.xcrp.2022.100931
– ident: e_1_2_8_25_1
  doi: 10.1038/s41428-022-00734-9
– ident: e_1_2_8_372_1
  doi: 10.1021/acsanm.4c00430
– ident: e_1_2_8_91_1
  doi: 10.1039/D3PY00395G
– ident: e_1_2_8_136_1
  doi: 10.1007/s11831-021-09700-9
– ident: e_1_2_8_101_1
  doi: 10.1063/5.0131067
– ident: e_1_2_8_405_1
  doi: 10.2147/IJN.S344208
– volume-title: Genetic Algorithms
  year: 2008
  ident: e_1_2_8_302_1
– ident: e_1_2_8_334_1
  doi: 10.1021/acsmacrolett.9b00039
– ident: e_1_2_8_272_1
  doi: 10.1109/ACCESS.2020.3018151
– ident: e_1_2_8_351_1
  doi: 10.1016/j.aej.2023.11.085
– ident: e_1_2_8_197_1
  doi: 10.1039/D4AN00658E
– ident: e_1_2_8_27_1
  doi: 10.1038/s41524-019-0221-0
– ident: e_1_2_8_407_1
  doi: 10.1126/sciadv.aav9784
– ident: e_1_2_8_248_1
  doi: 10.1186/2008-6695-4-7
– volume-title: Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner
  year: 2015
  ident: e_1_2_8_445_1
– ident: e_1_2_8_243_1
  doi: 10.1021/acsnano.8b02454
– ident: e_1_2_8_356_1
  doi: 10.1016/j.commatsci.2023.112479
– ident: e_1_2_8_344_1
  doi: 10.1016/j.ces.2020.116325
– ident: e_1_2_8_107_1
  doi: 10.1002/advs.202101864
– ident: e_1_2_8_322_1
  doi: 10.1021/acs.jpcc.3c00267
– ident: e_1_2_8_16_1
  doi: 10.1038/nmat1543
– ident: e_1_2_8_284_1
  doi: 10.1038/s42256-020-0166-5
– ident: e_1_2_8_417_1
  doi: 10.1016/j.commatsci.2020.110224
– ident: e_1_2_8_5_1
  doi: 10.1016/j.pmatsci.2016.05.001
– volume: 21
  start-page: 1263
  ident: e_1_2_8_53_1
  publication-title: IEEE Trans. Knowl. Data Eng. 2009
– ident: e_1_2_8_240_1
  doi: 10.1021/acsami.1c04017
– volume-title: Gaussian Processes for Machine Learning
  year: 2006
  ident: e_1_2_8_403_1
– volume-title: The Solubility of Nonelectrolytes
  year: 1950
  ident: e_1_2_8_330_1
– ident: e_1_2_8_413_1
  doi: 10.3390/polym16010115
– ident: e_1_2_8_81_1
  doi: 10.1021/ma5021209
– ident: e_1_2_8_412_1
  doi: 10.3390/ma12091475
– ident: e_1_2_8_308_1
  doi: 10.1201/9780203910115
– ident: e_1_2_8_438_1
  doi: 10.1021/acsmacrolett.2c00369
– ident: e_1_2_8_29_1
  doi: 10.1021/acspolymersau.1c00035
– ident: e_1_2_8_99_1
– ident: e_1_2_8_141_1
  doi: 10.3390/polym14020357
– ident: e_1_2_8_291_1
  doi: 10.1016/j.matpr.2021.02.730
– ident: e_1_2_8_373_1
  doi: 10.1021/jacs.1c05055
– ident: e_1_2_8_212_1
  doi: 10.1039/D1SM00725D
– ident: e_1_2_8_324_1
  doi: 10.1002/adfm.201501919
– ident: e_1_2_8_21_1
  doi: 10.1002/adma.202201809
– ident: e_1_2_8_277_1
  doi: 10.1021/acs.macromol.4c00508
– ident: e_1_2_8_42_1
– ident: e_1_2_8_36_1
  doi: 10.1088/1361-651X/aaf8ca
– ident: e_1_2_8_127_1
  doi: 10.1103/PhysRevE.99.043307
– ident: e_1_2_8_56_1
  doi: 10.1038/s41586-018-0337-2
– volume: 62
  start-page: 6239
  year: 2023
  ident: e_1_2_8_128_1
  publication-title: J. Polym. Sci.
– ident: e_1_2_8_263_1
  doi: 10.1016/j.ijleo.2018.03.121
– ident: e_1_2_8_402_1
  doi: 10.3390/pharmaceutics15020495
– ident: e_1_2_8_287_1
  doi: 10.3390/polym16081049
– ident: e_1_2_8_60_1
  doi: 10.1109/XLOOP49562.2019.00006
– ident: e_1_2_8_278_1
  doi: 10.1021/acs.jctc.3c01385
– ident: e_1_2_8_366_1
  doi: 10.1002/chem.202001505
– ident: e_1_2_8_140_1
  doi: 10.3390/polym14091802
– ident: e_1_2_8_262_1
  doi: 10.1111/j.2517-6161.1996.tb02080.x
– ident: e_1_2_8_290_1
  doi: 10.1021/acs.jcim.9b00807
– ident: e_1_2_8_411_1
  doi: 10.1021/ci010062o
– ident: e_1_2_8_72_1
  doi: 10.1038/nmat3568
– ident: e_1_2_8_295_1
  doi: 10.1145/3065386
– ident: e_1_2_8_346_1
  doi: 10.1002/macp.202300232
– ident: e_1_2_8_385_1
  doi: 10.1016/0022-5193(79)90085-7
– ident: e_1_2_8_368_1
  doi: 10.1007/978-3-030-05318-5
– ident: e_1_2_8_327_1
  doi: 10.1002/aenm.201900891
– ident: e_1_2_8_47_1
  doi: 10.1093/nar/gky1075
– ident: e_1_2_8_410_1
  doi: 10.1016/j.addr.2022.114172
– ident: e_1_2_8_142_1
  doi: 10.1097/01.psy.0000127692.23278.a9
– ident: e_1_2_8_249_1
  doi: 10.1016/j.compscitech.2006.07.026
– ident: e_1_2_8_399_1
  doi: 10.1016/j.ijhydene.2020.07.265
– ident: e_1_2_8_430_1
  doi: 10.1039/D0ME00020E
– ident: e_1_2_8_196_1
  doi: 10.1002/adem.202101072
– volume: 9
  start-page: 272
  year: 2012
  ident: e_1_2_8_254_1
  publication-title: Int. J. Comput. Sci.
– ident: e_1_2_8_26_1
  doi: 10.1021/acspolymersau.2c00037
– ident: e_1_2_8_143_1
  doi: 10.1002/wics.14
– ident: e_1_2_8_432_1
  doi: 10.1609/aaai.v35i1.16072
– ident: e_1_2_8_199_1
  doi: 10.1007/s10924-022-02557-4
– ident: e_1_2_8_40_1
  doi: 10.3390/polym14010026
– ident: e_1_2_8_161_1
  doi: 10.1007/s10822-022-00442-9
– ident: e_1_2_8_165_1
– ident: e_1_2_8_207_1
  doi: 10.1038/s41524-024-01328-0
– year: 2022
  ident: e_1_2_8_389_1
  publication-title: ChemRxiv
– ident: e_1_2_8_88_1
  doi: 10.1021/acspolymersau.3c00007
– ident: e_1_2_8_176_1
  doi: 10.1016/S0031-3203(01)00178-9
– ident: e_1_2_8_67_1
  doi: 10.1038/s41524-018-0081-z
– volume: 27
  start-page: 130
  year: 2015
  ident: e_1_2_8_184_1
  publication-title: Shanghai Arch. Psychiatry
– ident: e_1_2_8_202_1
  doi: 10.1177/0003702816675361
– volume-title: Compendium of Polymer Terminology and Nomenclature
  year: 2008
  ident: e_1_2_8_421_1
– ident: e_1_2_8_49_1
  doi: 10.1016/j.spl.2018.02.031
– ident: e_1_2_8_439_1
  doi: 10.1016/j.giant.2023.100171
– ident: e_1_2_8_66_1
  doi: 10.1186/1471-2105-14-106
– ident: e_1_2_8_168_1
  doi: 10.1063/5.0046854
– ident: e_1_2_8_398_1
  doi: 10.1007/978-1-59745-210-6
– volume-title: Polymeric Materials: Structure, Properties, Applications
  year: 2012
  ident: e_1_2_8_4_1
– ident: e_1_2_8_44_1
  doi: 10.1021/ed100697w
– ident: e_1_2_8_132_1
– ident: e_1_2_8_180_1
– ident: e_1_2_8_394_1
  doi: 10.1016/j.progpolymsci.2010.01.003
– volume: 2
  start-page: 78
  year: 2020
  ident: e_1_2_8_55_1
  publication-title: Front. Data Comput.
– ident: e_1_2_8_35_1
  doi: 10.1016/j.inffus.2019.12.001
– ident: e_1_2_8_17_1
  doi: 10.1103/PhysRevLett.71.3158
– ident: e_1_2_8_86_1
  doi: 10.1016/j.addr.2020.11.009
– ident: e_1_2_8_131_1
  doi: 10.1371/journal.pone.0161788
– volume-title: Applied Linear Statistical Models
  year: 1983
  ident: e_1_2_8_238_1
– ident: e_1_2_8_361_1
  doi: 10.1021/acsami.1c24715
– ident: e_1_2_8_293_1
  doi: 10.1016/j.renene.2023.01.017
– ident: e_1_2_8_408_1
  doi: 10.1038/s41467-022-35343-w
– ident: e_1_2_8_170_1
– ident: e_1_2_8_154_1
  doi: 10.1017/9781108348973
– ident: e_1_2_8_298_1
– ident: e_1_2_8_8_1
  doi: 10.1002/anie.201711105
– ident: e_1_2_8_95_1
  doi: 10.1016/j.trechm.2020.11.004
– ident: e_1_2_8_342_1
  doi: 10.1016/j.compbiomed.2020.104134
– ident: e_1_2_8_337_1
  doi: 10.1039/D4SM00590B
– ident: e_1_2_8_90_1
  doi: 10.1002/wcms.1603
– ident: e_1_2_8_183_1
  doi: 10.1142/S0219633608004416
– ident: e_1_2_8_397_1
  doi: 10.1039/D2PY01056A
– ident: e_1_2_8_125_1
  doi: 10.1186/s40537-022-00652-w
– ident: e_1_2_8_79_1
  doi: 10.1021/acsapm.0c00524
– ident: e_1_2_8_270_1
  doi: 10.1016/0098-3004(93)90090-R
– ident: e_1_2_8_23_1
  doi: 10.1038/s41598-021-85601-y
– ident: e_1_2_8_359_1
  doi: 10.1039/D3TA06385B
– volume-title: Gaussian Processes for Machine Learning
  year: 2006
  ident: e_1_2_8_150_1
– ident: e_1_2_8_229_1
  doi: 10.1023/A:1007379606734
– ident: e_1_2_8_224_1
  doi: 10.1038/s41524-023-01034-3
– ident: e_1_2_8_188_1
  doi: 10.1007/s00366-020-01226-1
– ident: e_1_2_8_37_1
  doi: 10.1109/EIDWT.2011.13
– ident: e_1_2_8_437_1
  doi: 10.1073/pnas.1607412113
– ident: e_1_2_8_75_1
  doi: 10.1021/acs.jpcb.0c00708
– ident: e_1_2_8_236_1
  doi: 10.3390/polym11081250
– ident: e_1_2_8_34_1
  doi: 10.1016/j.matt.2021.06.036
– ident: e_1_2_8_192_1
  doi: 10.1021/acsmacrolett.1c00521
– ident: e_1_2_8_376_1
  doi: 10.1016/j.matlet.2021.130622
SSID ssj0009606
Score 2.6099887
SecondaryResourceType review_article
Snippet Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical...
SourceID pubmedcentral
proquest
pubmed
crossref
wiley
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage e2413695
SubjectTerms chemical descriptors
Chemical synthesis
Chemists
Datasets
FAIR data
Machine learning
Mathematicians
Molecular weight distribution
Polymer chemistry
Polymers
Review
Title Machine Learning in Polymer Research
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fadma.202413695
https://www.ncbi.nlm.nih.gov/pubmed/39924835
https://www.proquest.com/docview/3178987487
https://www.proquest.com/docview/3165081589
https://pubmed.ncbi.nlm.nih.gov/PMC11923530
Volume 37
WOSCitedRecordID wos001419079100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVWIB
  databaseName: Wiley Online Library Full Collection 2020
  customDbUrl:
  eissn: 1521-4095
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0009606
  issn: 0935-9648
  databaseCode: DRFUL
  dateStart: 19980101
  isFulltext: true
  titleUrlDefault: https://onlinelibrary.wiley.com
  providerName: Wiley-Blackwell
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB58HfTg-1FfVBA8FZtH0-S4qIsHlUVU9lbSJNWFtSu7KvjvTdJu3UVE0EsfZEKaTqbztTP9BuCYWS9DGUKRwrGMaJHoiDNhj5A2RmjOtWfnf7hKb254tys6E3_xV_wQzQc3Zxn-ee0MXOaj0y_SUKk9b5CLCzGRzMI8QiR16xrTzhftLvPVNV20LxKM8jFtY4xPp_tPu6VvWPN7yuQklPW-qL3y_1mswnKNQ8NWtXDWYMaU67A0wU64AcfXPtHShDUH62PYK8POoP_xbIbhOGNvE-7aF3dnl1FdVCFS1g8lkWTEJLSQiSEqsaqRcapShAqDY1ZQhaSrgF5YlKap4TrX3O6ZMIzmKcJGky2YKwel2YHQoT1CtECFQjQnPC-YVFTlWKmcpoQGEI1vaaZqwnFX96KfVVTJOHOTz5rJB3DSyL9UVBs_Su6PNZTVJjfKLBDigqf2BSyAo6bZGouLgMjSDN6cjAOkKOEigO1Koc1QjqGXWjwaAJ9SdSPgiLinW8rekyfkRg4mJyQOAHtd_3L5Wev8utWc7f6l0x4sYleI2CfD7cPc6_DNHMCCen_tjYaH3gbsNu3yQ5g_v23fX30Cv28Huw
linkProvider Wiley-Blackwell
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3da9swED-6ZLDtod333GWbB4E9mVi2JEuPYW3oWBLCyEbejC3JXSBzRtoO9t9PJ38kIYxC6ZNtdLYsn873s-78O4A-t16GckICFYVZQAumA8Gl3SPaGKmF0I6d_8c4mU7FYiFndTYh_gtT8UO0C25oGe59jQaOC9KDLWtoph1xEAaGuGQPoEvtXGId6J59G30fb5l3uSuwiQG_QHIqGubGMBrsX2HfMx3AzcOsyV0069zR6OQeBvIUjmss6g-ryfMMjkz5HJ7sMBS-gP7EJVsav-ZhvfSXpT9br_7-Mhu_ydp7CfPR-fzzRVAXVgiU9UUsyHhsGC0yZmLFrHqyMFEJIYWJQl5QRTKsgl5YpKapETrXwm65NJzmCYmMjl9Bp1yX5g34iPjiWEtSKELzWOQFzxRVeaRUTpOYehA0zzRVNek41r5YpRVdcpTi4NN28B58auV_V3Qb_5XsNSpKa7O7Si0YElIk9iPMg49tszUYjIJkpVnfoAyCUsKE9OB1pdG2K2TppRaTeiD2dN0KIBn3fku5_OlIuQlCZRaHHkRO2bfcfjo8mwzbo9O7nPQBHl3MJ-N0_GX69S08jrAwsUuO60HnenNj3sFD9ed6ebV5X5vEP8vxCrA
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrZ1LS8QwEMcHX4gefD_qs4LgqWzTJmlyXNRFUZc9iHgrbZLqgnZlfYDf3kzarS4ignhqSxLadDrJv830NwCH3M4ylBMSqCjMAlowHQgu7R7RxkgthHZ0_pvLpNsVt7eyV0cT4r8wFR-i-eCGnuHGa3Rw86SL1ic1NNMOHIQLQ1yySZimzA60CHemvU_uLnfpNXG5L5CcihG3MYxa4-3H56VvYvN7zORXLesmo87iP3RjCRZqJeq3q0dnGSZMuQLzX_iEq3B45UItjV9TWO_8fun3Bg_vj2boj2L21uC6c3p9fBbUaRUCZWciFmQ8NowWGTOxYtY4WZiohJDCRCEvqCIZ5kAvrE7T1Aida2G3XBpO84RERsfrMFUOSrMJPuq9ONaSFIrQPBZ5wTNFVR4pldMkph4Eo3uaqho5jpkvHtIKlhyl2Pm06bwHR039pwq28WPNnZGJ0trpnlMrhYQUiX0F8-CgKbbugmsgWWkGr1gHJSlhQnqwUVm0ORUyeqlVpB6IMVs3FRDFPV5S9u8dkpugUGZx6EHkjP3L5aftk6t2c7T1l0b7MNs76aSX592LbZiLMCuxi4zbgamX4avZhRn19tJ_Hu45f_gAgpMImQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+Learning+in+Polymer+Research&rft.jtitle=Advanced+materials+%28Weinheim%29&rft.au=Ge%2C+Wei&rft.au=De+Silva%2C+Ramindu&rft.au=Fan%2C+Yanan&rft.au=Sisson%2C+Scott+A.&rft.date=2025-03-01&rft.issn=0935-9648&rft.eissn=1521-4095&rft.volume=37&rft.issue=11&rft_id=info:doi/10.1002%2Fadma.202413695&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_adma_202413695
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0935-9648&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0935-9648&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0935-9648&client=summon