De Novo Inverse Design Superhard C-N Compounds via Global Machine Learning Interatomic Potentials and Multiobjective Optimization Algorithm
A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C-N superhard materials. In this approach, an improved multio...
Gespeichert in:
| Veröffentlicht in: | The journal of physical chemistry letters Jg. 16; H. 18; S. 4392 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
08.05.2025
|
| ISSN: | 1948-7185, 1948-7185 |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C-N superhard materials. In this approach, an improved multiobjective optimization algorithm is introduced, utilizing structure similarity constraint to prevent convergence toward local minima. Combined with active learning, it trains global machine learning interatomic potentials (
-MLIPs) while exploring target materials. By comparing several
-MLIPs and selecting the best, the resulting
-MLIPs achieved reasonable precision within three iterations. Through multiple searches, 38 novel and stable C-N superhard materials not present in major computational materials databases were identified. Notably, the material C
(
6
22) with a hardness of 97.4 GPa was discovered, potentially exceeding that of diamond (94.0 GPa). This approach provided a new pathway for materials design with target properties. |
|---|---|
| AbstractList | A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C-N superhard materials. In this approach, an improved multiobjective optimization algorithm is introduced, utilizing structure similarity constraint to prevent convergence toward local minima. Combined with active learning, it trains global machine learning interatomic potentials (
-MLIPs) while exploring target materials. By comparing several
-MLIPs and selecting the best, the resulting
-MLIPs achieved reasonable precision within three iterations. Through multiple searches, 38 novel and stable C-N superhard materials not present in major computational materials databases were identified. Notably, the material C
(
6
22) with a hardness of 97.4 GPa was discovered, potentially exceeding that of diamond (94.0 GPa). This approach provided a new pathway for materials design with target properties. A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C-N superhard materials. In this approach, an improved multiobjective optimization algorithm is introduced, utilizing structure similarity constraint to prevent convergence toward local minima. Combined with active learning, it trains global machine learning interatomic potentials (g-MLIPs) while exploring target materials. By comparing several g-MLIPs and selecting the best, the resulting g-MLIPs achieved reasonable precision within three iterations. Through multiple searches, 38 novel and stable C-N superhard materials not present in major computational materials databases were identified. Notably, the material C3(P6422) with a hardness of 97.4 GPa was discovered, potentially exceeding that of diamond (94.0 GPa). This approach provided a new pathway for materials design with target properties.A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C-N superhard materials. In this approach, an improved multiobjective optimization algorithm is introduced, utilizing structure similarity constraint to prevent convergence toward local minima. Combined with active learning, it trains global machine learning interatomic potentials (g-MLIPs) while exploring target materials. By comparing several g-MLIPs and selecting the best, the resulting g-MLIPs achieved reasonable precision within three iterations. Through multiple searches, 38 novel and stable C-N superhard materials not present in major computational materials databases were identified. Notably, the material C3(P6422) with a hardness of 97.4 GPa was discovered, potentially exceeding that of diamond (94.0 GPa). This approach provided a new pathway for materials design with target properties. |
| Author | Cheng, Guanjian Yin, Wan-Jian |
| Author_xml | – sequence: 1 givenname: Guanjian surname: Cheng fullname: Cheng, Guanjian organization: Key Laboratory for Computational Physical Sciences (MOE), State Key Laboratory of Surface Physics, Department of Physics, Fudan University, Shanghai 200433, China – sequence: 2 givenname: Wan-Jian orcidid: 0000-0003-0932-2789 surname: Yin fullname: Yin, Wan-Jian organization: Hefei National Laboratory, Hefei 230088, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40273319$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkEtLAzEcxINU7EM_gSA5etmaZJ85llZroQ9BPZds9r9tym6yJtkF_Qp-aRes4GmG4TdzmDEaaKMBoVtKppQw-iCkm54aWYH301gSQjN6gUaUR1mQ0iwe_PNDNHbuREjCSZZeoWFEWBqGlI_Q9wLw1nQGr3QH1gFegFMHjV_bBuxR2ALPgy2em7oxrS4c7pTAy8rkosIbIY9KA16DsFrpQz_hwQpvaiXxi_GgvRKVw0IXeNNWXpn8BNKrDvCu8apWX6LPNJ5VB2OVP9bX6LLsC3Bz1gl6f3p8mz8H691yNZ-tA8F46oO85JTERSg48IRHcRaSMo3LhHEZQp4xGZUyLCXjWZ5xAVAA6_E8kTlNe6pkE3T_u9tY89GC8_taOQlVJTSY1u37Y6IkZiRhPXp3Rtu8hmLfWFUL-7n_O5D9AKWbeZk |
| ContentType | Journal Article |
| DBID | NPM 7X8 |
| DOI | 10.1021/acs.jpclett.5c00181 |
| DatabaseName | PubMed MEDLINE - Academic |
| DatabaseTitle | PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Chemistry |
| EISSN | 1948-7185 |
| ExternalDocumentID | 40273319 |
| Genre | Journal Article |
| GroupedDBID | 53G 55A 5VS 7~N AABXI ABBLG ABJNI ABLBI ABMVS ABQRX ABUCX ACGFO ACGFS ACS ADHLV AEESW AENEX AFEFF AHGAQ ALMA_UNASSIGNED_HOLDINGS AQSVZ BAANH CUPRZ DU5 EBS ED~ GGK GNL IH9 JG~ NPM P2P RNS ROL UI2 VF5 VG9 W1F XKZ 7X8 |
| ID | FETCH-LOGICAL-a297t-bf9105d3a9e96945830f75f629c3eb82c4fc3fc298b89aeede205db6cb17f62f2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001474268000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1948-7185 |
| IngestDate | Wed Jul 02 04:50:06 EDT 2025 Mon Jul 21 06:07:20 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 18 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a297t-bf9105d3a9e96945830f75f629c3eb82c4fc3fc298b89aeede205db6cb17f62f2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0003-0932-2789 |
| PMID | 40273319 |
| PQID | 3194652062 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_3194652062 pubmed_primary_40273319 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-05-08 |
| PublicationDateYYYYMMDD | 2025-05-08 |
| PublicationDate_xml | – month: 05 year: 2025 text: 2025-05-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | The journal of physical chemistry letters |
| PublicationTitleAlternate | J Phys Chem Lett |
| PublicationYear | 2025 |
| SSID | ssj0069087 |
| Score | 2.4579933 |
| Snippet | A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 4392 |
| Title | De Novo Inverse Design Superhard C-N Compounds via Global Machine Learning Interatomic Potentials and Multiobjective Optimization Algorithm |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40273319 https://www.proquest.com/docview/3194652062 |
| Volume | 16 |
| WOSCitedRecordID | wos001474268000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwELbagtReoKXl3WqQuAYS52WfKrSAuJCuBEh7W9mODbtik-0m7J_gTzPjZOFUCamXnMaRNR7Pw_P4GDvO0tJKx6PAUYVrYlQZ6NJEFPOIJNFCmDL0YBN5UYjRSA77B7emL6tc6USvqMva0Bv5KYpKkqU8zPjv-d-AUKMou9pDaHxkazG6MnQx89FrFgEDPw-QhytFgDo4XU0d4tGpMs3JdI4n07YnqSFouujfPqa3NZeb_7vLr2yj9zLhrBOLb-yDrbbY58EK3O07ez63UNTLGmjOxqKxcO4rOeDmaW4X1IgFg6AAUhYEu9TAcqKggweAa19-aaGfzHoP_lERY_fZxMCwbqn8CGUaVFWCb--t9bTTqvAH9dOsb_yEs8d73Hn7MPvB7i4vbgdXQY_LECgu8zbQDn2MtIyVtDKTlHgNXZ66jEsTWy24SZyJneFSaCEVGmHLkVxnRkc5Ujm-zT5VdWV3GeQqTDRHnzK3YeIEGo4M_2VCZWluT5ztsaMVn8fIIUpmqMrWT834jdN7bKc7rPG8G9AxTmhIDxLsv2P1AfvCCdKXahjFIVtzyCH7k62bZTtpFr-8QOG3GF6_APok2RI |
| linkProvider | ProQuest |
| 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=De+Novo+Inverse+Design+Superhard+C-N+Compounds+via+Global+Machine+Learning+Interatomic+Potentials+and+Multiobjective+Optimization+Algorithm&rft.jtitle=The+journal+of+physical+chemistry+letters&rft.au=Cheng%2C+Guanjian&rft.au=Yin%2C+Wan-Jian&rft.date=2025-05-08&rft.eissn=1948-7185&rft.volume=16&rft.issue=18&rft.spage=4392&rft_id=info:doi/10.1021%2Facs.jpclett.5c00181&rft_id=info%3Apmid%2F40273319&rft_id=info%3Apmid%2F40273319&rft.externalDocID=40273319 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1948-7185&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1948-7185&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1948-7185&client=summon |