Inferring the Physics of Structural Evolution of Multicomponent Polymers <italic style="font-style: italic">via</italic> Machine-Learning-Accelerated Method

Kai-Hua Zhang; Ying Jiang; Liang-Shun Zhang

doi:10.1007/s10118-023-2891-9

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Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method

RESEARCH ARTICLE | Updated：2023-08-24

- Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method
- Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method
- 高分子科学（英文版） 2023年41卷第9期页码：1377-1385
- Affiliations：
  
  a.School of Chemistry, Center of Soft Matter Physics and Its Applications, Beihang University, Beijing 100191, China
  b.Shanghai Key Laboratory of Advanced Polymeric Materials, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
- Author bio：
  
  yjiang@buaa.edu.cn (Y.J.)
  zhangls@ecust.edu.cn (L.S.Z.)
- Funds：
  
  This work was financially supported by the National Natural Science Foundation of China (Nos. 22073028, 21873029 and 22073004) and the Fundamental Research Funds for the Central Universities.;Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-023-2891-9.
- DOI：10.1007/s10118-023-2891-9
  中图分类号：
- 收稿：2022-09-22，
  
  修回：2022-10-13，
  
  录用：2022-10-20，
  
  网络首发：2022-12-29，
  
  纸质出版：2023-09-01
- Accepted：
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Zhang, K. H.; Jiang, Y.; Zhang, L. S. Inferring the physics of structural evolution of multicomponent polymers via machine-learning-accelerated method. Chinese J. Polym. Sci. 2023, 41, 1377–1385

Kai-Hua Zhang, Ying Jiang, Liang-Shun Zhang. Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method[J]. Chinese Journal of Polymer Science, 2023, 41(9): 1377-1385. DOI: 10.1007/s10118-023-2891-9.
Zhang, K. H.; Jiang, Y.; Zhang, L. S. Inferring the physics of structural evolution of multicomponent polymers via machine-learning-accelerated method. Chinese J. Polym. Sci. 2023, 41, 1377–1385 DOI： 10.1007/s10118-023-2891-9.

Kai-Hua Zhang, Ying Jiang, Liang-Shun Zhang. Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method[J]. Chinese Journal of Polymer Science, 2023, 41(9): 1377-1385. DOI: 10.1007/s10118-023-2891-9. DOI：

摘要

A machine-learning-based method is developed to accelerate the predictions of structural evolution of multicomponent polymers. Importantly

the data-driven method can also infer the latent growth laws of phase-separated microstructures of multicomponent polymers without the prior knowledge of the governing dynamics.

Abstract

Dynamic self-consistent field theory (DSCFT) is a fruitful approach for modeling the structural evolution and collective kinetics for a wide variety of multicomponent polymers. However

solving a set of DSCFT equations remains daunting because of high computational demand. Herein

a machine learning method

integrating low-dimensional representations of microstructures and long short-term memory neural networks

is used to accelerate the predictions of structural evolution of multicomponent polymers. It is definitively demonstrated that the neural-network-trained surrogate model has the capability to accurately forecast the structural evolution of homopolymer blends as well as diblock copolymers

without the requirement of “on-the-fly” solution of DSCFT equations. Importantly

the data-driven method can also infer the latent growth laws of phase-separated microstructures of multicomponent polymers through simply using a few of time sequences from their past

without the prior knowledge of the governing dynamics. Our study exemplifies how the machine-learning-accelerated method can be applied to understand and discover the physics of structural evolution in the complex polymer systems.

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references

Thurn-Albrecht,T.;Schotter,J.;Kastle,C.A.;Emley,N.;Shibauchi,T.;Krusin-Elbaum,L.;Guarini,K.;Black,C.T.;Tuominen,M.T.;Russell,T.P.Ultrahigh-densitynanowirearraysgrowninself-assembleddiblockcopolymertemplates. Science 2000 , 290 ,2126−2129..

Meuler,A.J.;Hillmyer,M.A.;Bates,F.S.Orderednetworkmesostructuresinblockpolymermaterials. Macromolecules 2009 , 42 ,7221−7250..

Müller,M.;Abetz,V.Nonequilibriumprocessesinpolymermembraneformation:theoryandexperiment. Chem. Rev. 2021 , 121 ,14189−14231..

Huang,C.H.;Zhu,Y.Y.;Man,X.K.Blockcopolymerthinfilms. Phys. Rep. 2021 , 932 ,1−36..

Fraaije,J.G.E.M.;vanVlimmeren,B.A.C.;Maurits,N.M.;Postma,M.;Evers,O.A.;Hoffmann,C.;Altevogt,P.;Goldbeck-Wood,G.Thedynamicmean-fielddensityfunctionalmethodanditsapplicationtothemesoscopicdynamicsofquenchedblockcopolymermelts. J. Chem. Phys. 1997 , 106 ,4260−4269..

Morita,H.;Kawakatsu,T.;Doi,M.Dynamicdensityfunctionalstudyonthestructureofthinpolymerblendfilmswithafreesurface. Macromolecules 2001 , 34 ,8777–8783..

Müller,M.;Schmid,F.Incorporatingfluctuationsanddynamicsinself-consistentfieldtheoriesforpolymerblends. Adv. Polym. Sci. 2005 , 185 ,1−58..

Wang,G.;Ren,Y.;Müller,M.Collectiveshort-timedynamicsinmulticomponentpolymermelts. Macromolecules 2019 , 52 ,7704−7720..

Sides,S.W.;Fredrickson,G.H.Parallelalgorithmfornumericalself-consistentfieldtheorysimulationsofblockcopolymerstructure. Polymer 2003 , 44 ,5859−5866..

Hall,D.M.;Lookman,T.;Fredrickson,G.H.;Banerjee,S.Hydrodynamicself-consistentfieldtheoryforinhomogeneouspolymermelts. Phys. Rev. Lett. 2006 , 97 ,114501..

Zhang,L.;Sevink,A.;Schmid,F.HybridlatticeBoltzmann/dynamicself-consistentfieldsimulationsofmicrophaseseparationandvesicleformationinblockcopolymersystems. Macromolecules 2011 , 44 ,9434−9447..

Arora,A.;Qin,J.;Morse,D.C.;Delaney,K.T.;Fredrickson,G.H.;Bates,F.S.;Dorfman,K.D.Broadlyaccessibleself-consistentfieldtheoryforblockpolymermaterialsdiscovery. Macromolecules 2016 , 49 ,4675−4690..

Mehta,P.;Wang,C.-H.;Day,A.G.R.;Richardson,C.;Bukov,M.;Fisher,C.K.;Schwab,D.J.Ahigh-bias,low-varianceintroductiontomachinelearningforphysicists. Phys. Rep. 2019 , 810 ,1−124..

Carleo,G.;Cirac,I.;Cranmer,K.;Daudet,L.;Schuld,M.;Tishby,N.;Vogt-Maranto,L.;Zdeborová,L.Machinelearningandthephysicalsciences. Rev. Mod. Phys. 2019 , 91 ,045002..

Brunton,S.L.;Noack,B.R.;Koumoutsakos,P.Machinelearningforfluidmechanics. Annu. Rev. Fluid Mech. 2020 , 52 ,477−508..

Keith,J.A.;Vassilev-Galindo,V.;Cheng,B.;Chmiela,S.;Gastegger,M.;Müller,K.R.;Tkatchenko,A.Combiningmachinelearningandcomputationalchemistryforpredictiveinsightsintochemicalsystems. Chem. Rev. 2021 , 121 ,9816−9872..

Colen,J.;Han,M.;Zhang,R.;Redford,S.A.;Lemma,L.M.;Morgan,L.;Ruijgrok,P.V.;Adkins,R.;Bryant,Z.;Dogic,Z.;Gardel,M.L.;dePablo,J.J.;Vitelli,V.Machinelearningactive-nematichydrodynamics. Proc. Natl. Acad. Sci. U. S. A. 2021 , 118 ,e2016708118..

Li,H.;Jin,Y.;Jiang,Y.;Chen,J.Z.Y.DeterminingthenonequilibriumcriticalityofaGardnertransition via ahybridstudyofmolecularsimulationsandmachinelearning. Proc. Natl. Acad. Sci. U. S. A. 2021 , 118 ,e2017392118..

LeCun,Y.;Bengio,Y.;Hinton,G.Deeplearning. Nature 2017 , 521 ,436−444..

Carrasquilla,J.;Melko,R.G.Machinelearningphasesofmatter. Nat. Phys. 2017 , 13 ,431−434..

Bapst,V.;Keck,T.;Grabska-Barwińska,A.;Donner,C.;Cubuk,E.D.;Schoenholz,S.S.;Obika,A.;Nelson,A.W.R.;Back,T.;Hassabis,D.;Kohli,P.Unveilingthepredictivepowerofstaticstructureinglassysystems. Nat. Phys. 2020 , 16 ,448−454..

Zhao,S.;Cai,T.;Zhang,L.;Li,W.;Lin,J.Autonomousconstructionofphasediagramsofblockcopolymersbytheory-assistedactivemachinelearning. ACS Macro Lett. 2021 , 10 ,598−602..

Coli,G.M.;Boattini,E.;Filion,L.;Dijkstra,M.Inversedesignofsoftmaterials via adeeplearning-basedevolutionarystrategy. Sci Adv. 2022 , 8 ,eabj6731..

Aoyagi,T.Deeplearningmodelforpredictingphasediagramsofblockcopolymers. Comput. Mater. Sci. 2021 , 188 ,110224..

Tu,K.;Huang,H.;Lee,S.;Lee,W.;Sun,Z.;Alexander-Katz,A.;Ross,C.A.Machinelearningpredictionsofblockcopolymerself-assembly. Adv. Mater. 2020 , 32 ,2005713..

Schneider,L.;dePablo,J.J.Combiningparticle-basedsimulationsandmachinelearningtounderstanddefectkineticsinthinfilmsofsymmetricdiblockcopolymers. Macromolecules 2021 , 54 ,10074−10085..

Pathak,J.;Hunt,B.;Girvan,M.;Lu,Z.;Ott,E.Model-freepredictionoflargespatiotemporallychaoticsystemsfromdata:areservoircomputingapproach. Phys. Rev. Lett. 2018 , 120 ,024102..

Raissi,M.;Perdikaris,P.;Karniadakis,G.E.Physics-informedneuralnetworks:adeeplearningframeworkforsolvingforwardandinverseproblemsinvolvingnonlinearpartialdifferentialequations. J. Comput. Phys. 2019 , 378 ,686−707..

Zhao,H.;Storey,B.D.;Braatz,R.D.;Bazant,M.Z.Learningthephysicsofpatternformationfromimages. Phys. Rev. Lett. 2020 , 124 ,060201..

Bar-Sinai,Y.;Hoyer,S.;Hickey,J.;Brenner,M.P.Learningdata-drivendiscretizationsforpartialdifferentialequations. Proc. Natl. Acad. Sci. U. S. A. 2019 , 116 ,15344−15348..

Kemeth,F.P.;Bertalan,T.;Thiem,T.;Dietrich,F.;Moon,S.J.;Laing,C.R.;Kevrekidis,I.G.Learningemergentpartialdifferentialequationsinalearnedemergentspace. Nat. Commun. 2022 , 13 ,3318..

Vlachas,P.R.;ByeonW.;Wan,Z.Y.;Sapsis,T.P.;Koumoutsakos,P.Data-drivenforecastingofhigh-dimensionalchaoticsystemswithlongshort-termmemorynetworks. Proc. R. Soc. A 2018 , 474 ,20170844..

Vlachas,P.R.;Arampatzis,G.;Uhler,C.;Koumoutsakos,P.Multiscalesimulationsofcomplexsystemsbylearningtheireffectivedynamics. Nat. Machine Intelligence 2022 , 4 ,359−366..

MontesdeOcaZapiain,D.,Stewart,J.A.;Dingreville,R.Acceleratingphase-field-basedmicrostructureevolutionpredictionsviasurrogatemodelstrainedbymachinelearningmethods. NPJ Comput. Mater. 2021 , 7 ,1−11..

Hu,C.;Martin,S.;Dingreville,R.Acceleratingphase-fieldpredictions via recurrentneuralnetworkslearningthemicrostructureevolutioninlatentspace. Comput. Methods Appl. Mech. Engrg. 2022 , 397 ,115128..

Qi,S.;Schmid,F.Dynamicdensityfunctionaltheoriesforinhomogeneouspolymersystemscomparedtobrowniandynamicssimulations. Macromolecules 2017 , 50 ,9831−9845..

Pearson,K.Onlinesandplanesofclosestfittosystemsofpointsinspace. Philos. Mag. 1901 , 2 ,559−572..

Jolliffe,I.T. Principal Component Analysis ;Wiley:Chichester,U.K., 2002 ..

Hochreiter,S.;Schmidhuber,J.Longshort-termmemory. Neural Comput. 1997 , 9 ,1735−1780..

Greff,K.;Srivastava,R.K.;Koutnik,J.;Steunebrink,B.R.;Schmidhuber,J.LSTM:asearchspaceodyssey. IEEE Trans. Neural Netw. Learn. Syst. 2017 , 28 ,2222−2232..

Cho,K.;VanMerriënboer,B.;Bahdanau,D.;Bengio,Y.Onthepropertiesofneuralmachinetranslation:encoder-decoderapproaches. 2014 ,arXivpreprintarXiv:1409.1259.

Cho,K.;VanMerriënboer,B.;Gulcehre,C.;Bahdanau,D.;Bougares,F.;Schwenk,H.;Bengio,Y.LearningphraserepresentationsusingRNNencoder-decoderforstatisticalmachinetranslation. 2014 ,arXivpreprintarXiv:1406.1078.

Bray,A.J.Theoryofphase-orderingkinetics. Adv. Phys. 1994 , 43 ,357−459..

Sung,L.;Karim,A.;Douglas,J.F.;Han,C.C.Dimensionalcrossoverinthephaseseparationkineticsofthinpolymerblendfilms. Phys. Rev. Lett. 1996 , 76 ,4368..

Siggia,E.D.Latestagesofspinodaldecompositioninbinarymixtures. Phys. Rev. A: At., Mol., Opt. Phys. 1979 , 20 ,595−602..

Shiwa,Y.;Taneike,T.;Yokojima,Y.Scalingbehaviorofblockcopolymersinspontaneousgrowthoflamellardomains. Phys. Rev. Lett. 1996 , 77 ,4378..

Bahiana,M.;Oono,Y.Celldynamicalsystemapproachtoblockcopolymers. Phys. Rev. A: At., Mol., Opt. Phys. 1990 , 41 ,6763..

Yokojima,Y.;Shiwa,Y.Orderingprocessinquenchedblockcopolymersatlowtemperatures. Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top. 2000 , 62 ,6838..

Singh,M.;Wu,W.;Nuka,V.;Strzalka,J.;Douglas,J.F.;Karim,A.Latestagedomaincoarseningdynamicsoflamellarblockcopolymers. ACS Macro Lett. 2021 , 10 ,727−731..

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Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method

Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method

DOI：10.1007/s10118-023-2891-9

摘要

Abstract

关键词

Keywords

references