GloMPO (Globally Managed Parallel Optimization): a tool for expensive, black-box optimizations, application to ReaxFF reparameterizations
In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimiz...
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| Published in: | Journal of cheminformatics Vol. 14; no. 1; pp. 7 - 29 |
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| Format: | Journal Article |
| Language: | English |
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| ISSN: | 1758-2946, 1758-2946 |
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| Abstract | In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager. |
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| AbstractList | In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager. In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager.In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager. Abstract In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager. In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager. Keywords: ReaxFF, Global optimization, Reparameterization, Black-box optimization, Python, Parallel computation |
| ArticleNumber | 7 |
| Audience | Academic |
| Author | Freitas Gustavo, Michael Verstraelen, Toon |
| Author_xml | – sequence: 1 givenname: Michael orcidid: 0000-0002-1832-8413 surname: Freitas Gustavo fullname: Freitas Gustavo, Michael organization: Center for Molecular Modeling, Ghent University, Software for Chemistry and Materials – sequence: 2 givenname: Toon orcidid: 0000-0001-9288-5608 surname: Verstraelen fullname: Verstraelen, Toon email: toon.verstraelen@ugent.be organization: Center for Molecular Modeling, Ghent University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35172881$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1287/IJOC.1.3.190 10.1002/jcc.23246 10.1016/J.ENGAPPAI.2019.02.003 10.1016/j.cplett.2006.03.003 10.1080/0892702031000104887 10.1007/S10710-014-9214-4 10.1109/SIS.2005.1501604 10.1007/3-540-58484-6_264 10.1002/jcc.24481 10.1021/acs.jctc.6b00461 10.1021/acs.jctc.7b01272 10.1145/2001858.2002123 10.1016/j.amc.2003.11.023 10.1111/ITOR.12001 10.1134/S0040577916040139 10.1021/acs.jpcc.7b09948 10.1007/978-3-642-13800-3_27 10.1162/106365601750190398 10.1145/2001576.2001808 10.1021/jp405992m 10.1021/jp911867r 10.1016/J.COMPTC.2016.09.032 10.1007/s00158-009-0420-2 10.1002/(SICI)1096-987X(199912)20:16<1752::AID-JCC7>3.0.CO;2-0 10.1021/acs.jctc.9b00769 10.1016/j.asoc.2015.04.061 10.1007/s10898-007-9149-x 10.1021/acs.jctc.7b00445 10.1007/3-540-44864-0_91 10.1002/nme.1960 10.1021/jp970984n 10.4236/am.2012.330215 10.1016/j.commatsci.2019.109393 10.1021/acs.jctc.8b00151 10.1002/jcc.23382 10.1007/978-1-4757-4137-7_11 10.1038/npjcompumats.2015.11 10.1021/acs.jctc.7b00870 10.1038/s41592-019-0686-2 10.1007/s11047-008-9098-4 10.1016/S0098-1354(98)00251-8 10.1016/j.ejor.2012.10.012 10.1021/acs.jcim.1c00333 10.1039/FT9949002881 10.1007/0-306-48126-X_4 10.1016/j.chemphys.2020.110888 10.1007/BFb0029787 10.1109/CEC.2001.934312 10.1021/jp004368u 10.1007/3-540-46033-0_19 10.1007/978-3-642-17390-5_4 10.3390/inorganics5040064 10.1109/CEC.2013.6557585 10.1088/0953-8984/21/8/084208 10.1002/jcc.23966 10.1007/s11590-016-1037-1 10.1021/jp709896w 10.1109/TEVC.2016.2627581 10.32614/rj-2013-002 10.1016/j.ins.2019.09.065 10.1142/S021821300600262X 10.1007/978-3-540-30217-9_29 |
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| Keywords | ReaxFF Global optimization Parallel computation Reparameterization Black-box optimization Python |
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| References | KarabogaDBasturkBA powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithmJ Global Optim200739345947110.1007/s10898-007-9149-x XiangYGubianSSuomelaBHoengJGeneralized simulated annealing for global optimization: the GenSA packageR J201351132810.32614/rj-2013-002 ElyasafASipperMSoftware review: the HeuristicLab frameworkGenet Program Evolvable Mach201415221521810.1007/S10710-014-9214-4 DieterichJMHartkeBEmpirical review of standard benchmark functions using evolutionary global optimizationAppl Math201231552156410.4236/am.2012.330215 SörensenKMetaheuristics-the metaphor exposedInt Trans Oper Res201522131810.1111/ITOR.12001 BianchiLDorigoMGambardellaLMGutjahrWJA survey on metaheuristics for stochastic combinatorial optimizationNat Comput20098223928710.1007/s11047-008-9098-4 Hansen N (2011) Injecting external solutions into CMA-ES. arXiv:1110.4181 DieterichJHartkeBImproved cluster structure optimization: hybridizing evolutionary algorithms with local heat pulsesInorganics201754641:CAS:528:DC%2BC1cXitVCmtrzN10.3390/inorganics5040064 Schlierkamp-Voosen D, Mühlenbein H (1994) Strategy adaptation by competing subpopulations. Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 866 LNCS, p 199–208. https://doi.org/10.1007/3-540-58484-6_264 Lukasiewycz M, Glaß M, Reimann F, Teich J (2011) Opt4J: a modular framework for meta-heuristic optimization. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation—GECCO ’11, ACM Press, New York, p 1723. https://doi.org/10.1145/2001576.2001808 Dyer D (2010) Watchmaker framework for evolutionary computing. https://watchmaker.uncommons.org Kronfeld M, Planatscher H, Zell A (2010) The EvA2 optimization framework. Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6073 LNCS, p 247–250, https://doi.org/10.1007/978-3-642-13800-3_27 HubinPOJacqueminDLeherteLVercauterenDPParameterization of the ReaxFF reactive force field for a proline-catalyzed aldol reactionJ Comput Chem20163729256425721:CAS:528:DC%2BC28XhsVOqtr7K10.1002/jcc.2448127592688 Van DuinACTDasguptaSLorantFGoddardWAReaxFF: a reactive force field for hydrocarbonsJ Phys Chem A200110541939694091:CAS:528:DC%2BD3MXmvFChu78%3D10.1021/jp004368u StepanovaMMShefovKSSlavyanovSYMultifactorial global search algorithm in the problem of optimizing a reactive force fieldTheoretical Math Phys (Russian Federation)2016187160361710.1134/S0040577916040139 Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: 2005 IEEE Swarm Intelligence Symposium, SIS 2005, p 68–75. https://doi.org/10.1109/SIS.2005.1501604 DittnerMMüllerJAktulgaHMHartkeBEfficient global optimization of reactive force-field parametersJ Comput Chem20153620155015611:CAS:528:DC%2BC2MXhtVensr3J10.1002/jcc.2396626085201 HuXSchusterJSchulzSEMultiparameter and parallel optimization of ReaxFF reactive force field for modeling the atomic layer deposition of copperJ Phys Chem C20171215028,07728,0891:CAS:528:DC%2BC2sXhvV2mtLfN10.1021/acs.jpcc.7b09948 GagnéCParizeauMGenericity in evolutionary computation software tools: principles and case-studyInt J Artif Intell Tools200615217319410.1142/S021821300600262X SenftleTPHongSIslamMMKylasaSBZhengYShinYKJunkermeierCEngel-HerbertRJanikMJAktulgaHMVerstraelenTGramaAVan DuinACTThe ReaxFF reactive force-field: development, applications and future directionsnpj Comput Mater2016215,0111:CAS:528:DC%2BC2sXlslantL4%3D10.1038/npjcompumats.2015.11 DittnerMHartkeBGlobally optimal catalytic fields—inverse design of abstract embeddings for maximum reaction rate accelerationJ Chem Theory Comput2018147354735641:CAS:528:DC%2BC1cXhtFSrt7%2FE10.1021/acs.jctc.8b0015129883539 KomissarovLRügerRHellströmMVerstraelenTParAMS: parameter optimization for atomistic and molecular simulationsJ Chem Inf Model2021618373737431:CAS:528:DC%2BB3MXhtVOis7vN10.1021/acs.jcim.1c0033333983727 Parejo JA, Racero J, Guerrero F, Kwok T, Smith KA (2003) FOM: a framework for metaheuristic optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2660 LNCS, p 886–895. https://doi.org/10.1007/3-540-44864-0_91 SalaRBaldanziniNPieriniMGlobal optimization test problems based on random field compositionOptimization Lett20171169971310.1007/s11590-016-1037-1 TrnkaTTvaroškaIKočaJAutomated training of ReaxFF reactive force fields for energetics of enzymatic reactionsJ Chem Theory Comput20181412913021:CAS:528:DC%2BC2sXhvVars7bM10.1021/acs.jctc.7b0087029156140 Swersky K, Snoek J, Adams RP (2014) Freeze-thaw Bayesian optimization. http://arxiv.org/abs/1406.3896 HanagandiVNikolaouMA hybrid approach to global optimization using a clustering algorithm in a genetic search frameworkComput Chem Eng19982212191319251:CAS:528:DyaK1MXkslSktQ%3D%3D10.1016/S0098-1354(98)00251-8 ChenowethKVan DuinACTGoddardWAReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidationJ Phys Chem A20081125104010531:CAS:528:DC%2BD1cXmtFOrtw%3D%3D10.1021/jp709896w18197648 MartíRResendeMGCRibeiroCCMulti-start methods for combinatorial optimizationEur J Oper Res201322611810.1016/j.ejor.2012.10.012 SaudLJMohamedMJInvestigating the guidance feature of searching in the genetic algorithmIraqi J Comput Commun Control Syst Eng20141412134 WeiLZhaoMA niche hybrid genetic algorithm for global optimization of continuous multimodal functionsAppl Math Comput2005160364966110.1016/j.amc.2003.11.023 LarssonHRVan DuinACTHartkeBGlobal optimization of parameters in the reactive force field ReaxFF for SiOHJ Comput Chem20133425217821891:CAS:528:DC%2BC3sXhtFShu7nK10.1002/jcc.2338223852672 ShanSWangGGSurvey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functionsStruct Multidiscip Optim201041221924110.1007/s00158-009-0420-2 Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. In: Evolutionary computation, vol 9(2). MIT Press, p 159–195, https://doi.org/10.1162/106365601750190398 PorterBXueFNiche evolution strategy for global optimizationProc IEEE Conf Evol Comput ICEC200121086109210.1109/CEC.2001.934312 GongYChenWZhanZZhangJLiYZhangQLiJDistributed evolutionary algorithms and their models: a survey of the state-of-the-artAppl Soft Comput20153428630010.1016/j.asoc.2015.04.061 BaeGTAikensCMImproved ReaxFF force field parameters for Au-S-C-H systemsJ Phys Chem A20131174010,43810,4461:CAS:528:DC%2BC3sXhsVGjt7vE10.1021/jp405992m Tung L (2020) Programming language Python’s popularity: ahead of Java for first time but still trailing C. https://zd.net/3C17olF Keijzer M, Merelo JJ, Romero G, Schoenauer M (2001) Evolving objects: a general purpose evolutionary computation library. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2310 LNCS:231–242. https://doi.org/10.1007/3-540-46033-0_19 YangMZhouALiCGuanJYanXCCFR2: a more efficient cooperative co-evolutionary framework for large-scale global optimizationInf Sci2020512647910.1016/j.ins.2019.09.065 RossiGFerrandoRSearching for low-energy structures of nanoparticles: a comparison of different methods and algorithmsJ Phys Condens Matter2009218084,2081:CAS:528:DC%2BD1MXjsVWrtbc%3D10.1088/0953-8984/21/8/084208 Freitas Gustavo M (2020) Globally managed parallel optimization. GitHub repository. https://github.com/mfgustavo/glompo VirtanenPGommersROliphantTEHaberlandMReddyTCournapeauDBurovskiEPetersonPWeckesserWBrightJvan der WaltSJBrettMWilsonJMillmanKJMayorovNNelsonARJJonesEKernRLarsonECareyCJPolatIFengYMooreEWVanderPlasJLaxaldeDPerktoldJCimrmanRHenriksenIQuinteroEAHarrisCRArchibaldAMRibeiroAHPedregosaFvan MulbregtPSciPy v1 ContributorsSciPy 1.0: fundamental algorithms for scientific computing in PythonNat Methods2020172612721:CAS:528:DC%2BB3cXislCjuro%3D10.1038/s41592-019-0686-23201554332015543 FurmanDCarmeliBZeiriYKosloffREnhanced particle swarm optimization algorithm: efficient training of ReaxFF reactive force fieldsJ Chem Theory Comput2018146310031121:CAS:528:DC%2BC1cXptVCitb8%3D10.1021/acs.jctc.7b0127229727570 IypeEHütterMJansenAPJNedeaSVRindtCCMParameterization of a reactive force field using a Monte-Carlo algorithmJ Comput Chem20133413114311541:CAS:528:DC%2BC3sXislyls7g%3D10.1002/jcc.2324623420666 Hansen N, Kern S (2004) Evaluating the CMA evolution strategy on multimodal test functions. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 3242 LNCS. Springer, p 282–291, https://doi.org/10.1007/978-3-540-30217-9_29 SCM, van Duin ACT, Goddard WA, Islam MM, van Schoot H, Trnka T, Yakovlev AL (2020) ReaxAMS 2020 (r89496). https://scm.com Fink A, Voß S (2002) Hotframe: a heuristic optimization framework. In: Voß S, Woodruff DL (eds) Optimization Software Class Libraries. Springer, Boston, p 81–154. https://doi.org/10.1007/0-306-48126-X_4 SchutteJFHaftkaRTFreglyBJImproved global convergence probability using multiple independent optimizationsInt J Numer Meth Eng200771667870210.1002/nme.1960 OptTek (2021) OptQuest. https://www.opttek.com/products/optquest YangMOmidvarMNLiCLiXCaiZKazimipourBYaoXEfficient resource allocation in cooperative co-evolution for large-scale global optimizationIEEE Trans Evol Comput201721449350510.1109/TEVC.2016.2627581 Barrera J, Coello Coello CA (2011) Test function generators for assessing the performance of PSO algorithms in multimodal optimization. In: Panigrahi BK, Shi Y, Lim M (eds) Handbook of Swarm Intelligence: concepts, Principles and Applications, Springer, Berlin Heidelberg, p 89–117, https://doi.org/10.1007/978-3-642-17390-5_4 ShchygolGYakovlevATrnkaTVan DuinACTVerstraelenTReaxFF parameter optimization with Monte-Carlo and evolutionary algorithms: guide MR Labrosse (581_CR37) 2010; 114 R Martí (581_CR42) 2013; 226 L Komissarov (581_CR35) 2021; 61 M Yang (581_CR70) 2017; 21 J Dieterich (581_CR8) 2017; 5 G Shchygol (581_CR58) 2019; 15 581_CR53 Y Xiang (581_CR69) 2013; 5 MM Stepanova (581_CR60) 2016; 187 T Trnka (581_CR62) 2018; 14 581_CR45 581_CR44 G Rossi (581_CR49) 2006; 423 J Müller (581_CR43) 2016; 12 581_CR48 M Dittner (581_CR9) 2017; 1107 JF Schutte (581_CR54) 2007; 71 A Elyasaf (581_CR14) 2014; 15 M Dittner (581_CR11) 2015; 36 ACT Van Duin (581_CR64) 1994; 90 581_CR41 D Karaboga (581_CR33) 2007; 39 581_CR36 581_CR34 581_CR39 F Guo (581_CR22) 2020; 172 K Chenoweth (581_CR6) 2008; 112 JD Gale (581_CR19) 2003; 29 R Sala (581_CR51) 2017; 11 F Glover (581_CR20) 1989; 1 L Wei (581_CR68) 2005; 160 K Sörensen (581_CR59) 2015; 22 Y Gong (581_CR21) 2015; 34 PO Hubin (581_CR31) 2016; 37 DJ Wales (581_CR67) 1997; 101 581_CR1 A Ramírez (581_CR47) 2019; 81 TP Senftle (581_CR56) 2016; 2 581_CR4 ACT Van Duin (581_CR65) 2001; 105 C Gagné (581_CR18) 2006; 15 581_CR25 581_CR24 L Bianchi (581_CR5) 2009; 8 S Shan (581_CR57) 2010; 41 M Yang (581_CR71) 2020; 512 581_CR29 581_CR28 581_CR27 581_CR26 E Iype (581_CR32) 2013; 34 B Porter (581_CR46) 2001; 2 LJ Saud (581_CR52) 2014; 14 G Rossi (581_CR50) 2009; 21 V Hanagandi (581_CR23) 1998; 22 581_CR61 P Virtanen (581_CR66) 2020; 17 GT Bae (581_CR2) 2013; 117 G Barcaro (581_CR3) 2017; 13 X Hu (581_CR30) 2017; 121 581_CR63 HR Larsson (581_CR38) 2013; 34 581_CR13 581_CR12 D Furman (581_CR17) 2018; 14 581_CR55 Y Liu (581_CR40) 2020; 538 581_CR16 581_CR15 M Dittner (581_CR10) 2018; 14 JM Dieterich (581_CR7) 2012; 3 |
| References_xml | – reference: Rapin J, Teytaud O (2018) Nevergrad—a gradient-free optimization platform (v0.4.0.post3). GitHub repository. https://github.com/FacebookResearch/Nevergrad – reference: BianchiLDorigoMGambardellaLMGutjahrWJA survey on metaheuristics for stochastic combinatorial optimizationNat Comput20098223928710.1007/s11047-008-9098-4 – reference: GagnéCParizeauMGenericity in evolutionary computation software tools: principles and case-studyInt J Artif Intell Tools200615217319410.1142/S021821300600262X – reference: HuXSchusterJSchulzSEMultiparameter and parallel optimization of ReaxFF reactive force field for modeling the atomic layer deposition of copperJ Phys Chem C20171215028,07728,0891:CAS:528:DC%2BC2sXhvV2mtLfN10.1021/acs.jpcc.7b09948 – reference: Dorne R, Voudouris C (2004) HSF: the iOpt’s framework to easily design metaheuristic methods. In: Metaheuristics: computer decision-making. Springer, Boston, p 237–256, https://doi.org/10.1007/978-1-4757-4137-7_11 – reference: Ali MZ, Awad NH, Reynolds RG (2013) Hybrid niche cultural algorithm for numerical global optimization. In: 2013 IEEE Congress on Evolutionary Computation, New York, IEEE. p 309–316, https://doi.org/10.1109/CEC.2013.6557585 – reference: Barrera J, Coello Coello CA (2011) Test function generators for assessing the performance of PSO algorithms in multimodal optimization. In: Panigrahi BK, Shi Y, Lim M (eds) Handbook of Swarm Intelligence: concepts, Principles and Applications, Springer, Berlin Heidelberg, p 89–117, https://doi.org/10.1007/978-3-642-17390-5_4 – reference: MüllerJHartkeBReaxFF reactive force field for disulfide mechanochemistry, fitted to multireference ab initio dataJ Chem Theory Comput2016128391339251:CAS:528:DC%2BC28XhtFKrtL%2FM10.1021/acs.jctc.6b0046127415976 – reference: Dyer D (2010) Watchmaker framework for evolutionary computing. https://watchmaker.uncommons.org/ – reference: ShchygolGYakovlevATrnkaTVan DuinACTVerstraelenTReaxFF parameter optimization with Monte-Carlo and evolutionary algorithms: guidelines and insightsJ Chem Theory Comput20191512679968121:CAS:528:DC%2BC1MXitVGmsL%2FM10.1021/acs.jctc.9b0076931657217 – reference: GloverFTabu search—part IORSA J Comput19891319020610.1287/IJOC.1.3.190 – reference: LabrosseMRJohnsonJKVan DuinACTDevelopment of a transferable reactive force field for cobaltJ Phys Chem A201011418585558611:CAS:528:DC%2BC3cXkslKksL0%3D10.1021/jp911867r20394398 – reference: ShanSWangGGSurvey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functionsStruct Multidiscip Optim201041221924110.1007/s00158-009-0420-2 – reference: Swersky K, Snoek J, Adams RP (2014) Freeze-thaw Bayesian optimization. http://arxiv.org/abs/1406.3896 – reference: Keijzer M, Merelo JJ, Romero G, Schoenauer M (2001) Evolving objects: a general purpose evolutionary computation library. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2310 LNCS:231–242. https://doi.org/10.1007/3-540-46033-0_19 – reference: Lukasiewycz M, Glaß M, Reimann F, Teich J (2011) Opt4J: a modular framework for meta-heuristic optimization. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation—GECCO ’11, ACM Press, New York, p 1723. https://doi.org/10.1145/2001576.2001808 – reference: MartíRResendeMGCRibeiroCCMulti-start methods for combinatorial optimizationEur J Oper Res201322611810.1016/j.ejor.2012.10.012 – reference: SenftleTPHongSIslamMMKylasaSBZhengYShinYKJunkermeierCEngel-HerbertRJanikMJAktulgaHMVerstraelenTGramaAVan DuinACTThe ReaxFF reactive force-field: development, applications and future directionsnpj Comput Mater2016215,0111:CAS:528:DC%2BC2sXlslantL4%3D10.1038/npjcompumats.2015.11 – reference: RossiGFerrandoRSearching for low-energy structures of nanoparticles: a comparison of different methods and algorithmsJ Phys Condens Matter2009218084,2081:CAS:528:DC%2BD1MXjsVWrtbc%3D10.1088/0953-8984/21/8/084208 – reference: SCM, van Duin ACT, Goddard WA, Islam MM, van Schoot H, Trnka T, Yakovlev AL (2020) ReaxAMS 2020 (r89496). https://scm.com – reference: LiuYHuJHouHWangBDevelopment and application of a ReaxFF reactive force field for molecular dynamics of perfluorinatedketones thermal decompositionChem Phys20205381108881:CAS:528:DC%2BB3cXhtFyjsrrF10.1016/j.chemphys.2020.110888 – reference: RossiGFerrandoRGlobal optimization by excitable walkersChem Phys Lett20064231–317221:CAS:528:DC%2BD28XktlGksLk%3D10.1016/j.cplett.2006.03.003 – reference: GaleJDRohlALThe general utility lattice program (GULP)Mol Simul20032952913411:CAS:528:DC%2BD3sXjtlSntLo%3D10.1080/0892702031000104887 – reference: Parejo JA, Racero J, Guerrero F, Kwok T, Smith KA (2003) FOM: a framework for metaheuristic optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2660 LNCS, p 886–895. https://doi.org/10.1007/3-540-44864-0_91 – reference: Hartke B (1999) Global cluster geometry optimization by a phenotype algorithm with niches: location of elusive minima, and low-order scaling with cluster size. J Comput Chem 20:1752–1759. https://doi.org/10.1002/(SICI)1096-987X(199912)20:16<1752::AID-JCC7>3.0.CO;2-0. – reference: Tung L (2020) Programming language Python’s popularity: ahead of Java for first time but still trailing C. https://zd.net/3C17olF – reference: GuoFWenYSFengSQLiXDLiHSCuiSXZhangZRHuHQZhangGQChengXLIntelligent-ReaxFF: evaluating the reactive force field parameters with machine learningComput Mater Sci20201721093931:CAS:528:DC%2BC1MXitFGgu7bN10.1016/j.commatsci.2019.109393 – reference: DieterichJMHartkeBEmpirical review of standard benchmark functions using evolutionary global optimizationAppl Math201231552156410.4236/am.2012.330215 – reference: Van DuinACTBaasJMAVan De GraafBDelft molecular mechanics: a new approach to hydrocarbon force fields. Inclusion of a geometry-dependent charge calculationJ Chem Soc Faraday Trans199490192881289510.1039/FT9949002881 – reference: OptTek (2021) OptQuest. https://www.opttek.com/products/optquest/ – reference: TrnkaTTvaroškaIKočaJAutomated training of ReaxFF reactive force fields for energetics of enzymatic reactionsJ Chem Theory Comput20181412913021:CAS:528:DC%2BC2sXhvVars7bM10.1021/acs.jctc.7b0087029156140 – reference: KarabogaDBasturkBA powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithmJ Global Optim200739345947110.1007/s10898-007-9149-x – reference: LarssonHRVan DuinACTHartkeBGlobal optimization of parameters in the reactive force field ReaxFF for SiOHJ Comput Chem20133425217821891:CAS:528:DC%2BC3sXhtFShu7nK10.1002/jcc.2338223852672 – reference: VirtanenPGommersROliphantTEHaberlandMReddyTCournapeauDBurovskiEPetersonPWeckesserWBrightJvan der WaltSJBrettMWilsonJMillmanKJMayorovNNelsonARJJonesEKernRLarsonECareyCJPolatIFengYMooreEWVanderPlasJLaxaldeDPerktoldJCimrmanRHenriksenIQuinteroEAHarrisCRArchibaldAMRibeiroAHPedregosaFvan MulbregtPSciPy v1 ContributorsSciPy 1.0: fundamental algorithms for scientific computing in PythonNat Methods2020172612721:CAS:528:DC%2BB3cXislCjuro%3D10.1038/s41592-019-0686-23201554332015543 – reference: WeiLZhaoMA niche hybrid genetic algorithm for global optimization of continuous multimodal functionsAppl Math Comput2005160364966110.1016/j.amc.2003.11.023 – reference: HanagandiVNikolaouMA hybrid approach to global optimization using a clustering algorithm in a genetic search frameworkComput Chem Eng19982212191319251:CAS:528:DyaK1MXkslSktQ%3D%3D10.1016/S0098-1354(98)00251-8 – reference: ElyasafASipperMSoftware review: the HeuristicLab frameworkGenet Program Evolvable Mach201415221521810.1007/S10710-014-9214-4 – reference: ChenowethKVan DuinACTGoddardWAReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidationJ Phys Chem A20081125104010531:CAS:528:DC%2BD1cXmtFOrtw%3D%3D10.1021/jp709896w18197648 – reference: DittnerMMüllerJAktulgaHMHartkeBEfficient global optimization of reactive force-field parametersJ Comput Chem20153620155015611:CAS:528:DC%2BC2MXhtVensr3J10.1002/jcc.2396626085201 – reference: Hansen N, Kern S (2004) Evaluating the CMA evolution strategy on multimodal test functions. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 3242 LNCS. Springer, p 282–291, https://doi.org/10.1007/978-3-540-30217-9_29 – reference: GongYChenWZhanZZhangJLiYZhangQLiJDistributed evolutionary algorithms and their models: a survey of the state-of-the-artAppl Soft Comput20153428630010.1016/j.asoc.2015.04.061 – reference: KomissarovLRügerRHellströmMVerstraelenTParAMS: parameter optimization for atomistic and molecular simulationsJ Chem Inf Model2021618373737431:CAS:528:DC%2BB3MXhtVOis7vN10.1021/acs.jcim.1c0033333983727 – reference: WalesDJDoyeJPKGlobal optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atomsJ Phys Chem A199710128511151161:CAS:528:DyaK2sXktVGrurY%3D10.1021/jp970984n – reference: DieterichJHartkeBImproved cluster structure optimization: hybridizing evolutionary algorithms with local heat pulsesInorganics201754641:CAS:528:DC%2BC1cXitVCmtrzN10.3390/inorganics5040064 – reference: SchutteJFHaftkaRTFreglyBJImproved global convergence probability using multiple independent optimizationsInt J Numer Meth Eng200771667870210.1002/nme.1960 – reference: Van DuinACTDasguptaSLorantFGoddardWAReaxFF: a reactive force field for hydrocarbonsJ Phys Chem A200110541939694091:CAS:528:DC%2BD3MXmvFChu78%3D10.1021/jp004368u – reference: SalaRBaldanziniNPieriniMGlobal optimization test problems based on random field compositionOptimization Lett20171169971310.1007/s11590-016-1037-1 – reference: Schlierkamp-Voosen D, Mühlenbein H (1994) Strategy adaptation by competing subpopulations. Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 866 LNCS, p 199–208. https://doi.org/10.1007/3-540-58484-6_264 – reference: DittnerMHartkeBConquering the hard cases of Lennard-Jones clusters with simple recipesComput Theor Chem201711077131:CAS:528:DC%2BC28Xhs1emsbrF10.1016/J.COMPTC.2016.09.032 – reference: FurmanDCarmeliBZeiriYKosloffREnhanced particle swarm optimization algorithm: efficient training of ReaxFF reactive force fieldsJ Chem Theory Comput2018146310031121:CAS:528:DC%2BC1cXptVCitb8%3D10.1021/acs.jctc.7b0127229727570 – reference: Hoffmeister F, Bäck T (1991) Genetic algorithms and evolution strategies: similarities and differences. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 496 LNCS. Springer, Verlag. p 455–469. https://doi.org/10.1007/BFb0029787 – reference: BaeGTAikensCMImproved ReaxFF force field parameters for Au-S-C-H systemsJ Phys Chem A20131174010,43810,4461:CAS:528:DC%2BC3sXhsVGjt7vE10.1021/jp405992m – reference: YangMZhouALiCGuanJYanXCCFR2: a more efficient cooperative co-evolutionary framework for large-scale global optimizationInf Sci2020512647910.1016/j.ins.2019.09.065 – reference: HubinPOJacqueminDLeherteLVercauterenDPParameterization of the ReaxFF reactive force field for a proline-catalyzed aldol reactionJ Comput Chem20163729256425721:CAS:528:DC%2BC28XhsVOqtr7K10.1002/jcc.2448127592688 – reference: Hansen N, Baudis P, Akimoto Y (2019) CMA-ES, covariance matrix adaptation evolution strategy for non-linear numerical optimization in Python (v2.7.0). PyPI Project. https://pypi.org/project/cma/2.7.0/ – reference: Fink A, Voß S (2002) Hotframe: a heuristic optimization framework. In: Voß S, Woodruff DL (eds) Optimization Software Class Libraries. Springer, Boston, p 81–154. https://doi.org/10.1007/0-306-48126-X_4 – reference: Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: 2005 IEEE Swarm Intelligence Symposium, SIS 2005, p 68–75. https://doi.org/10.1109/SIS.2005.1501604 – reference: RamírezARomeroJRGarcía-MartínezCVenturaSJCLEC-MO: a Java suite for solving many-objective optimization engineering problemsEng Appl Artif Intell201981142810.1016/J.ENGAPPAI.2019.02.003 – reference: Kronfeld M, Planatscher H, Zell A (2010) The EvA2 optimization framework. Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6073 LNCS, p 247–250, https://doi.org/10.1007/978-3-642-13800-3_27 – reference: Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. In: Evolutionary computation, vol 9(2). MIT Press, p 159–195, https://doi.org/10.1162/106365601750190398 – reference: YangMOmidvarMNLiCLiXCaiZKazimipourBYaoXEfficient resource allocation in cooperative co-evolution for large-scale global optimizationIEEE Trans Evol Comput201721449350510.1109/TEVC.2016.2627581 – reference: XiangYGubianSSuomelaBHoengJGeneralized simulated annealing for global optimization: the GenSA packageR J201351132810.32614/rj-2013-002 – reference: StepanovaMMShefovKSSlavyanovSYMultifactorial global search algorithm in the problem of optimizing a reactive force fieldTheoretical Math Phys (Russian Federation)2016187160361710.1134/S0040577916040139 – reference: BarcaroGMontiSSementaLCarravettaVParametrization of a reactive force field (ReaxFF) for molecular dynamics simulations of Si nanoparticlesJ Chem Theory Comput2017138385438611:CAS:528:DC%2BC2sXhtVClsrnL10.1021/acs.jctc.7b0044528640604 – reference: PorterBXueFNiche evolution strategy for global optimizationProc IEEE Conf Evol Comput ICEC200121086109210.1109/CEC.2001.934312 – reference: Hansen N (2011) Injecting external solutions into CMA-ES. arXiv:1110.4181 – reference: Freitas Gustavo M (2020) Globally managed parallel optimization. GitHub repository. https://github.com/mfgustavo/glompo – reference: SaudLJMohamedMJInvestigating the guidance feature of searching in the genetic algorithmIraqi J Comput Commun Control Syst Eng20141412134 – reference: DittnerMHartkeBGlobally optimal catalytic fields—inverse design of abstract embeddings for maximum reaction rate accelerationJ Chem Theory Comput2018147354735641:CAS:528:DC%2BC1cXhtFSrt7%2FE10.1021/acs.jctc.8b0015129883539 – reference: SörensenKMetaheuristics-the metaphor exposedInt Trans Oper Res201522131810.1111/ITOR.12001 – reference: IypeEHütterMJansenAPJNedeaSVRindtCCMParameterization of a reactive force field using a Monte-Carlo algorithmJ Comput Chem20133413114311541:CAS:528:DC%2BC3sXislyls7g%3D10.1002/jcc.2324623420666 – volume: 1 start-page: 190 issue: 3 year: 1989 ident: 581_CR20 publication-title: ORSA J Comput doi: 10.1287/IJOC.1.3.190 – volume: 34 start-page: 1143 issue: 13 year: 2013 ident: 581_CR32 publication-title: J Comput Chem doi: 10.1002/jcc.23246 – volume: 81 start-page: 14 year: 2019 ident: 581_CR47 publication-title: Eng Appl Artif Intell doi: 10.1016/J.ENGAPPAI.2019.02.003 – volume: 423 start-page: 17 issue: 1–3 year: 2006 ident: 581_CR49 publication-title: Chem Phys Lett doi: 10.1016/j.cplett.2006.03.003 – volume: 29 start-page: 291 issue: 5 year: 2003 ident: 581_CR19 publication-title: Mol Simul doi: 10.1080/0892702031000104887 – volume: 14 start-page: 21 issue: 1 year: 2014 ident: 581_CR52 publication-title: Iraqi J Comput Commun Control Syst Eng – volume: 15 start-page: 215 issue: 2 year: 2014 ident: 581_CR14 publication-title: Genet Program Evolvable Mach doi: 10.1007/S10710-014-9214-4 – ident: 581_CR39 doi: 10.1109/SIS.2005.1501604 – ident: 581_CR53 doi: 10.1007/3-540-58484-6_264 – ident: 581_CR27 – volume: 37 start-page: 2564 issue: 29 year: 2016 ident: 581_CR31 publication-title: J Comput Chem doi: 10.1002/jcc.24481 – volume: 12 start-page: 3913 issue: 8 year: 2016 ident: 581_CR43 publication-title: J Chem Theory Comput doi: 10.1021/acs.jctc.6b00461 – volume: 14 start-page: 3100 issue: 6 year: 2018 ident: 581_CR17 publication-title: J Chem Theory Comput doi: 10.1021/acs.jctc.7b01272 – ident: 581_CR24 doi: 10.1145/2001858.2002123 – volume: 160 start-page: 649 issue: 3 year: 2005 ident: 581_CR68 publication-title: Appl Math Comput doi: 10.1016/j.amc.2003.11.023 – volume: 22 start-page: 3 issue: 1 year: 2015 ident: 581_CR59 publication-title: Int Trans Oper Res doi: 10.1111/ITOR.12001 – ident: 581_CR55 – volume: 187 start-page: 603 issue: 1 year: 2016 ident: 581_CR60 publication-title: Theoretical Math Phys (Russian Federation) doi: 10.1134/S0040577916040139 – volume: 121 start-page: 28,077 issue: 50 year: 2017 ident: 581_CR30 publication-title: J Phys Chem C doi: 10.1021/acs.jpcc.7b09948 – ident: 581_CR36 doi: 10.1007/978-3-642-13800-3_27 – ident: 581_CR26 doi: 10.1162/106365601750190398 – ident: 581_CR41 doi: 10.1145/2001576.2001808 – volume: 117 start-page: 10,438 issue: 40 year: 2013 ident: 581_CR2 publication-title: J Phys Chem A doi: 10.1021/jp405992m – volume: 114 start-page: 5855 issue: 18 year: 2010 ident: 581_CR37 publication-title: J Phys Chem A doi: 10.1021/jp911867r – ident: 581_CR61 – volume: 1107 start-page: 7 year: 2017 ident: 581_CR9 publication-title: Comput Theor Chem doi: 10.1016/J.COMPTC.2016.09.032 – volume: 41 start-page: 219 issue: 2 year: 2010 ident: 581_CR57 publication-title: Struct Multidiscip Optim doi: 10.1007/s00158-009-0420-2 – ident: 581_CR28 doi: 10.1002/(SICI)1096-987X(199912)20:16<1752::AID-JCC7>3.0.CO;2-0 – volume: 15 start-page: 6799 issue: 12 year: 2019 ident: 581_CR58 publication-title: J Chem Theory Comput doi: 10.1021/acs.jctc.9b00769 – volume: 34 start-page: 286 year: 2015 ident: 581_CR21 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2015.04.061 – volume: 39 start-page: 459 issue: 3 year: 2007 ident: 581_CR33 publication-title: J Global Optim doi: 10.1007/s10898-007-9149-x – volume: 13 start-page: 3854 issue: 8 year: 2017 ident: 581_CR3 publication-title: J Chem Theory Comput doi: 10.1021/acs.jctc.7b00445 – ident: 581_CR45 doi: 10.1007/3-540-44864-0_91 – volume: 71 start-page: 678 issue: 6 year: 2007 ident: 581_CR54 publication-title: Int J Numer Meth Eng doi: 10.1002/nme.1960 – volume: 101 start-page: 5111 issue: 28 year: 1997 ident: 581_CR67 publication-title: J Phys Chem A doi: 10.1021/jp970984n – volume: 3 start-page: 1552 year: 2012 ident: 581_CR7 publication-title: Appl Math doi: 10.4236/am.2012.330215 – volume: 172 start-page: 393 issue: 109 year: 2020 ident: 581_CR22 publication-title: Comput Mater Sci doi: 10.1016/j.commatsci.2019.109393 – ident: 581_CR13 – volume: 14 start-page: 3547 issue: 7 year: 2018 ident: 581_CR10 publication-title: J Chem Theory Comput doi: 10.1021/acs.jctc.8b00151 – volume: 34 start-page: 2178 issue: 25 year: 2013 ident: 581_CR38 publication-title: J Comput Chem doi: 10.1002/jcc.23382 – ident: 581_CR12 doi: 10.1007/978-1-4757-4137-7_11 – volume: 2 start-page: 15,011 year: 2016 ident: 581_CR56 publication-title: npj Comput Mater doi: 10.1038/npjcompumats.2015.11 – volume: 14 start-page: 291 issue: 1 year: 2018 ident: 581_CR62 publication-title: J Chem Theory Comput doi: 10.1021/acs.jctc.7b00870 – volume: 17 start-page: 261 year: 2020 ident: 581_CR66 publication-title: Nat Methods doi: 10.1038/s41592-019-0686-2 – ident: 581_CR48 – ident: 581_CR44 – volume: 8 start-page: 239 issue: 2 year: 2009 ident: 581_CR5 publication-title: Nat Comput doi: 10.1007/s11047-008-9098-4 – volume: 22 start-page: 1913 issue: 12 year: 1998 ident: 581_CR23 publication-title: Comput Chem Eng doi: 10.1016/S0098-1354(98)00251-8 – volume: 226 start-page: 1 issue: 1 year: 2013 ident: 581_CR42 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2012.10.012 – volume: 61 start-page: 3737 issue: 8 year: 2021 ident: 581_CR35 publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.1c00333 – volume: 90 start-page: 2881 issue: 19 year: 1994 ident: 581_CR64 publication-title: J Chem Soc Faraday Trans doi: 10.1039/FT9949002881 – ident: 581_CR15 doi: 10.1007/0-306-48126-X_4 – volume: 538 start-page: 888 issue: 110 year: 2020 ident: 581_CR40 publication-title: Chem Phys doi: 10.1016/j.chemphys.2020.110888 – ident: 581_CR16 – ident: 581_CR29 doi: 10.1007/BFb0029787 – volume: 2 start-page: 1086 year: 2001 ident: 581_CR46 publication-title: Proc IEEE Conf Evol Comput ICEC doi: 10.1109/CEC.2001.934312 – volume: 105 start-page: 9396 issue: 41 year: 2001 ident: 581_CR65 publication-title: J Phys Chem A doi: 10.1021/jp004368u – ident: 581_CR34 doi: 10.1007/3-540-46033-0_19 – ident: 581_CR4 doi: 10.1007/978-3-642-17390-5_4 – volume: 5 start-page: 64 issue: 4 year: 2017 ident: 581_CR8 publication-title: Inorganics doi: 10.3390/inorganics5040064 – ident: 581_CR1 doi: 10.1109/CEC.2013.6557585 – volume: 21 start-page: 084,208 issue: 8 year: 2009 ident: 581_CR50 publication-title: J Phys Condens Matter doi: 10.1088/0953-8984/21/8/084208 – volume: 36 start-page: 1550 issue: 20 year: 2015 ident: 581_CR11 publication-title: J Comput Chem doi: 10.1002/jcc.23966 – ident: 581_CR63 – volume: 11 start-page: 699 year: 2017 ident: 581_CR51 publication-title: Optimization Lett doi: 10.1007/s11590-016-1037-1 – volume: 112 start-page: 1040 issue: 5 year: 2008 ident: 581_CR6 publication-title: J Phys Chem A doi: 10.1021/jp709896w – volume: 21 start-page: 493 issue: 4 year: 2017 ident: 581_CR70 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2016.2627581 – volume: 5 start-page: 13 issue: 1 year: 2013 ident: 581_CR69 publication-title: R J doi: 10.32614/rj-2013-002 – volume: 512 start-page: 64 year: 2020 ident: 581_CR71 publication-title: Inf Sci doi: 10.1016/j.ins.2019.09.065 – volume: 15 start-page: 173 issue: 2 year: 2006 ident: 581_CR18 publication-title: Int J Artif Intell Tools doi: 10.1142/S021821300600262X – ident: 581_CR25 doi: 10.1007/978-3-540-30217-9_29 |
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| SubjectTerms | Algorithms Black-box optimization Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Documentation and Information in Chemistry Global optimization Hybridization Minima Optimization Parallel computation Python ReaxFF Reparameterization Software Theoretical and Computational Chemistry Workflow |
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| Title | GloMPO (Globally Managed Parallel Optimization): a tool for expensive, black-box optimizations, application to ReaxFF reparameterizations |
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