Applications of Machine Learning Methods in the Studies of Polymer Glass Formation

Zhen-yue Yang; Wen-jian Nie; Lun-yang Liu; Xiao-lei Xu; Wen-jie Xia; Wen-sheng Xu

doi:10.11777/j.issn1000-3304.2022.22367

您当前的位置：

首页 >

文章列表页 >

Applications of Machine Learning Methods in the Studies of Polymer Glass Formation

Review | 更新时间：2023-03-08

- Applications of Machine Learning Methods in the Studies of Polymer Glass Formation
- ACTA POLYMERICA SINICA Vol. 54, Issue 4, Pages: 432-450(2023)
- 作者机构：
  
  1.中国科学院长春应用化学研究所高分子物理与化学国家重点实验室长春 130022
  2.Department of Civil， Construction and Environmental Engineering， North Dakota State University， Fargo， North Dakota 58108
- 作者简介：
- 基金信息：
- DOI：10.11777/j.issn1000-3304.2022.22367
  CLC：
- Published：20 April 2023，
  
  Published Online：03 January 2023，
  
  Received：01 November 2022，
  
  Revised：17 November 2022，
扫描看全文
杨镇岳,聂文建,刘伦洋等.机器学习方法在高分子玻璃化研究中的应用,[J].高分子学报,2023,54(04):432-450.

Yang Zhen-yue,Nie Wen-jian,Liu Lun-yang,et al.Applications of Machine Learning Methods in the Studies of Polymer Glass Formation[J].ACTA POLYMERICA SINICA,2023,54(04):432-450.
杨镇岳,聂文建,刘伦洋等.机器学习方法在高分子玻璃化研究中的应用,[J].高分子学报,2023,54(04):432-450. DOI： 10.11777/j.issn1000-3304.2022.22367.

Yang Zhen-yue,Nie Wen-jian,Liu Lun-yang,et al.Applications of Machine Learning Methods in the Studies of Polymer Glass Formation[J].ACTA POLYMERICA SINICA,2023,54(04):432-450. DOI： 10.11777/j.issn1000-3304.2022.22367.

摘要

高分子玻璃的物理性质与其结构和动力学密切相关. 揭示高分子玻璃化的微观物理图像对高分子玻璃材料的结构调控和分子设计至关重要. 然而，高分子的长链结构和复杂单体结构特征致使目前仍然缺乏普适的理论或者模型来定量解释高分子玻璃化的物理机制. 因此，亟需发展更为先进的研究方法从而更深入地理解高分子玻璃化. 近年来，国内外学者利用基于数据驱动的信息学方法(例如机器学习)对高分子玻璃化开展了研究，并取得了丰富成果. 本综述首先介绍了常用的高分子信息学数据库和机器学习算法. 之后，从高分子玻璃化转变温度的预测、新型高分子玻璃材料的研发、过冷液体的结构-动力学关系和玻璃体系相变的确定四个方面总结和评述了机器学习应用在玻璃化研究中的代表性进展. 最后，探讨了机器学习方法在高分子玻璃化研究中面临的主要挑战，并对玻璃信息学这一领域的发展进行了展望.

Abstract

The physical properties of polymer glasses are closely related to their structure and dynamics. A deep understanding of the microscopic physical mechanism of polymer glass formation is crucial for the structural control and molecular design of polymer glasses. However

various molecular characteristics associated with complex segmental structures and chain topology of polymers impose significant challenges for the development of a fully predictive theory to describe their glass formation in a quantitative way. Hence

it is highly desired to develop more advanced approaches to better understand and predict polymer glass formation. In recent years

growing efforts have been made to shed new lights on polymer glass formation based on the data-driven informatics approaches

such as machine learning

and important progresses have been made towards this direction. The present review first introduces common polymer databases and machine learning algorithms

followed by a summary and review of the representative progresses on the applications of machine learning methods in the studies of polymer glass formation. In particular

the focus is placed on the prediction of the glass transition temperature

the research and development of novel glassy polymer materials

the investigation of structure-dynamics relationships of glass-forming liquids

and the determination of phase transitions of glasses. Finally

the present review discusses challenges and opportunities in the applications of machine learning methods to polymer glass formation and provides a perspective on glass informatics.

关键词

信息学高分子玻璃化机器学习理论模拟

Keywords

InformaticsPolymer glass formationMachine learningTheory and simulation

references

Soutis C. Carbon fiber reinforced plastics in aircraft construction. Mater. Sci. Eng. A, 2005, 412(1), 171-176. doi:10.1016/j.msea.2005.08.064http://dx.doi.org/10.1016/j.msea.2005.08.064

Wuttig M.; Yamada N. Phase-change materials for rewriteable data storage. Nat. Mater., 2007, 6(11), 824-832. doi:10.1038/nmat2009http://dx.doi.org/10.1038/nmat2009

Forrest, S. R.; Thompson, M. E. Introduction: Organic electronics and optoelectronics. Chem. Rev., 2007, 107(4), 923-925. doi:10.1021/cr0501590http://dx.doi.org/10.1021/cr0501590

Carta M.; Malpass-Evans R.; Croad M.; Rogan Y.; Jansen J. C.; Bernardo P.; Bazzarelli F.; McKeown N. B. An efficient polymer molecular sieve for membrane gas separations. Science, 2013, 339(6117), 303-307. doi:10.1126/science.1228032http://dx.doi.org/10.1126/science.1228032

Audus D. J.; de Pablo J. J. Polymer informatics: Opportunities and challenges. ACS Macro Lett., 2017, 6(10), 1078-1082. doi:10.1021/acsmacrolett.7b00228http://dx.doi.org/10.1021/acsmacrolett.7b00228

Fox T. G.; Flory P. J. Second-order transition temperatures and related properties of polystyrene. I. Influence of molecular weight. J. Appl. Phys., 1950, 21(6), 581-591. doi:10.1063/1.1699711http://dx.doi.org/10.1063/1.1699711

Doolittle, A. K. Studies in Newtonian flow. I. The Dependence of the viscosity of liquids on temperature. J. Appl. Phys., 1951, 22(8), 1031-1035. doi:10.1063/1.1700096http://dx.doi.org/10.1063/1.1700096

Fox, T. G. Flory, P. J. Further studies on the melt viscosity of polyisobutylene. J. Phys. Chem., 1951, 55(2), 221-234. doi:10.1021/j150485a010http://dx.doi.org/10.1021/j150485a010

Cohen M. H.; Turnbull D. Molecular transport in liquids and glasses. J. Chem. Phys., 1959, 31(5), 1164-1169. doi:10.1063/1.1730566http://dx.doi.org/10.1063/1.1730566

Turnbull D.; Cohen M. H. Free-volume model of the amorphous phase: glass transition. J. Chem. Phys., 1961, 34(1), 120-125. doi:10.1063/1.1731549http://dx.doi.org/10.1063/1.1731549

Gibbs J. H.; DiMarzio E. A. Nature of the glass transition and the glassy state. J. Chem. Phys., 1958, 28(3), 373-383. doi:10.1063/1.1744141http://dx.doi.org/10.1063/1.1744141

Bestul A. B.; Chang S. S. Excess entropy at glass transformation. J. Chem. Phys., 1964, 40(12), 3731-3733. doi:10.1063/1.1725086http://dx.doi.org/10.1063/1.1725086

Adam G.; Gibbs J. H. On the temperature dependence of cooperative relaxation properties in glass-forming liquids. J. Chem. Phys., 1965, 43(1), 139-146. doi:10.1063/1.1696442http://dx.doi.org/10.1063/1.1696442

Götze W. Complex dynamics of glass-forming liquids a mode-coupling theory. Oxford: Oxford University Press, 2009. doi:10.1093/acprof:oso/9780199235346.003.0004http://dx.doi.org/10.1093/acprof:oso/9780199235346.003.0004

Fredrickson G. H.; Andersen H. C. Kinetic Ising model of the glass transition. Phys. Rev. Lett., 1984, 53(13), 1244-1247. doi:10.1103/physrevlett.53.1244http://dx.doi.org/10.1103/physrevlett.53.1244

Mezard M.; Parisi G.; Virasoro M. Spin Glass Theory and Beyond. Singapore: World Scientific, 1987. doi:10.1142/0271http://dx.doi.org/10.1142/0271

Dyre J. C.; Olsen N. B.; Christensen T. Local elastic expansion model for viscous-flow activation energies of glass-forming molecular liquids. Phys. Rev. B, 1996, 53(5), 2171-2174. doi:10.1103/physrevb.53.2171http://dx.doi.org/10.1103/physrevb.53.2171

Dyre J. C.; Christensen T.; Olsen N. B. Elastic models for the non-Arrhenius viscosity of glass-forming liquids. J. Non Cryst. Solids, 2006, 352(42-49), 4635-4642. doi:10.1016/j.jnoncrysol.2006.02.173http://dx.doi.org/10.1016/j.jnoncrysol.2006.02.173

Dyre, J. C. Colloquium: The glass transition and elastic models of glass-forming liquids. Rev. Mod. Phys., 2006, 78(3), 953-972. doi:10.1103/revmodphys.78.953http://dx.doi.org/10.1103/revmodphys.78.953

Parisi G. Infinite number of order parameters for spin-glasses. Phys. Rev. Lett., 1979, 43(23), 1754-1756. doi:10.1103/physrevlett.43.1754http://dx.doi.org/10.1103/physrevlett.43.1754

Parisi G. A sequence of approximated solutions to the S-K model for spin glasses. J. Phys. A: Math. Gen., 1980, 13(4), L115-L121. doi:10.1088/0305-4470/13/4/009http://dx.doi.org/10.1088/0305-4470/13/4/009

Chen K.; Saltzman E. J.; Schweizer K. S. Molecular theories of segmental dynamics and mechanical response in deeply supercooled polymer melts and glasses. Annu. Rev. Condens. Matter Phys., 2010, 1, 277-300. doi:10.1146/annurev-conmatphys-070909-104110http://dx.doi.org/10.1146/annurev-conmatphys-070909-104110

Schweizer K. S.; Saltzman E. J. Theory of dynamic barriers, activated hopping, and the glass transition in polymer melts. J. Chem. Phys., 2004, 121(4), 1984-2000. doi:10.1063/1.1756854http://dx.doi.org/10.1063/1.1756854

Saltzman E. J.; Schweizer K. S. Universal scaling, dynamic fragility, segmental relaxation, and vitrification in polymer melts. J. Chem. Phys., 2004, 121(4), 2001-2009. doi:10.1063/1.1756856http://dx.doi.org/10.1063/1.1756856

Simmons D. S.; Cicerone M. T.; Zhong Q.; Tyagi M.; Douglas J. F. Generalized localization model of relaxation in glass-forming liquids. Soft Matter, 2012, 8(45), 11455-11461. doi:10.1039/c2sm26694fhttp://dx.doi.org/10.1039/c2sm26694f

Pazmiño Betancourt B. A.; Hanakata P. Z.; Starr F. W.; Douglas J. F. Quantitative relations between cooperative motion, emergent elasticity, and free volume in model glass-forming polymer materials. Proc. Natl. Acad. Sci. U.S.A., 2015, 112(10), 2966-2971. doi:10.1073/pnas.1418654112http://dx.doi.org/10.1073/pnas.1418654112

Douglas J. F.; Pazmino Betancourt B. A.; Tong X. H.; Zhang H. Localization model description of diffusion and structural relaxation in glass-forming Cu-Zr alloys. J. Stat. Mech., 2016, 2016(5), 054048. doi:10.1088/1742-5468/2016/05/054048http://dx.doi.org/10.1088/1742-5468/2016/05/054048

Dudowicz J.; Freed K. F.; Douglas J. F. Generalized entropy theory of polymer glass formation. Adv. Chem. Phys., 2008, 137, 125-222. doi:10.1002/9780470238080.ch3http://dx.doi.org/10.1002/9780470238080.ch3

Xu W. S.; Douglas J. F.; Sun Z. Y. Polymer glass formation: Role of activation free energy, configurational entropy, and collective motion. Macromolecules, 2021, 54(7), 3001-3033. doi:10.1021/acs.macromol.0c02740http://dx.doi.org/10.1021/acs.macromol.0c02740

Ferguson A. L. Machine learning and data science in soft materials engineering. J. Phys.: Condens. Matter, 2017, 30(4), 043002. doi:10.1088/1361-648x/aa98bdhttp://dx.doi.org/10.1088/1361-648x/aa98bd

李云琦, 刘伦洋, 陈文多, 安立佳. 材料基因组学的发展现状、研究思路与建议. 中国科学: 化学, 2018, 48(3), 243-255.

都仕, 张宋奇, 王立权, 林嘉平, 杜磊. 高分子材料基因组: 高分子研发的新方法. 高分子学报, 2022, 53(6), 592-607. doi:10.11777/j.issn1000-3304.2021.21404http://dx.doi.org/10.11777/j.issn1000-3304.2021.21404

王天尧, 李剑锋. 深度学习在蛋白质结构预测中的应用及启示. 高分子学报, 2022, 53(6), 581-591. doi:10.11777/j.issn1000-3304.2021.21401http://dx.doi.org/10.11777/j.issn1000-3304.2021.21401

宫祥瑞, 蒋滢. 机器学习在高分子材料基因组研究中的进展与挑战. 高分子学报, 2022, 53(11), 1287-1300. doi:10.11777/j.issn1000-3304.2022.22094http://dx.doi.org/10.11777/j.issn1000-3304.2022.22094

侯冠一, 刘军, 张立群. 计算材料学在高分子材料领域的研究进展与发展趋势. 高分子学报, DOI: 10.11777/j.issn1000-3304.2022.22181http://dx.doi.org/10.11777/j.issn1000-3304.2022.22181.

吴佳琦, 孙奕韬, 汪卫华, 李茂枝. 机器学习在非晶材料中的应用. 中国科学: 物理学力学天文学, 2020, 50(6), 7-20.

Shetty P.; Ramprasad R. Automated knowledge extraction from polymer literature using natural language processing. iScience, 2021, 24(1), 101922. doi:10.1016/j.isci.2020.101922http://dx.doi.org/10.1016/j.isci.2020.101922

Otsuka S.; Kuwajima I.; Hosoya J.; Xu Y. B.; Yamazaki M. PoLyInfo: polymer database for polymeric materials design. Tirana2011 International Conference on Emerging Intelligent Data and Web Technologies., Albania: IEEE, 2011. 22-29. doi:10.1109/eidwt.2011.13http://dx.doi.org/10.1109/eidwt.2011.13

Chen L. H.; Pilania G.; Batra R.; Huan T. D.; Kim C.; Kuenneth C.; Ramprasad R. Polymer informatics: Current status and critical next steps. Mater. Sci. Eng. R Rep., 2021, 144, 100595. doi:10.1016/j.mser.2020.100595http://dx.doi.org/10.1016/j.mser.2020.100595

Sha W. X.; Li Y.; Tang S.; Tian J.; Zhao Y. M.; Guo Y. Q.; Zhang W. X.; Zhang X. F.; Lu S. F.; Cao Y. C.; Cheng S. J. Machine learning in polymer informatics. InfoMat, 2021, 3(4), 353-361. doi:10.1002/inf2.12167http://dx.doi.org/10.1002/inf2.12167

刘伦洋, 丁芳, 李云琦. 高分子材料大数据研究:共性基础、进展及挑战. 高分子学报, 2022, 53(6), 564-580. doi:10.11777/j.issn1000-3304.2021.21360http://dx.doi.org/10.11777/j.issn1000-3304.2021.21360

Batra R.; Song L.; Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning. Nat. Rev. Mater., 2021, 6(8), 655-678. doi:10.1038/s41578-020-00255-yhttp://dx.doi.org/10.1038/s41578-020-00255-y

Brandrup J.; Immergut E. H.; Grulke E. A.; Abe A.; Bloch D. R. Handbook Polymer. New York: Wiley, 1999.

Bicerano J. Prediction of Polymer Properties. 3rd ed. New York: Marcel Dekker, 2002. doi:10.1201/9780203910115http://dx.doi.org/10.1201/9780203910115

Ellis B.; Smith R. Polymers: Property Database A, 2nd ed. Florida: CRC Press, 2008. doi:10.1365/s10337-010-1620-zhttp://dx.doi.org/10.1365/s10337-010-1620-z

Mark, J. E. Polymer Data Handbook. New York: Oxford University Press, 2009.

Van Krevelen D. W.; Te Nijenhuis, K. Properties of Polymers. Amsterdam: Elsevier, 2009.

Braun D.; Cherdron H.; Rehahn M.; Ritter H.; Voit B. Polymer Synthesis: Theory and Practice. Berlin: Springer, 2012. doi:10.1007/978-3-642-28980-4http://dx.doi.org/10.1007/978-3-642-28980-4

Wypych, G. Handbook of Polymers. 2nd ed. Toronto: ChemTec Publishing, 2016.

Cubuk E. D.; Schoenholz S. S.; Kaxiras E.; Liu A. J. Structural properties of defects in glassy liquids. J. Phys. Chem. B, 2016, 120(26), 6139-6146. doi:10.1021/acs.jpcb.6b02144http://dx.doi.org/10.1021/acs.jpcb.6b02144

Sussman D. M.; Schoenholz S. S.; Cubuk E. D.; Liu A. J. Disconnecting structure and dynamics in glassy thin films. Proc. Natl. Acad. Sci. U.S.A., 2017, 114(40), 10601-10605. doi:10.1073/pnas.1703927114http://dx.doi.org/10.1073/pnas.1703927114

Schoenholz S. S.; Cubuk E. D.; Kaxiras E.; Liu A. J. Relationship between local structure and relaxation in out-of-equilibrium glassy systems. Proc. Natl. Acad. Sci. U.S.A., 2017, 114(2), 263-267. doi:10.1073/pnas.1610204114http://dx.doi.org/10.1073/pnas.1610204114

Landes F. P.; Biroli G.; Dauchot O.; Liu A. J.; Reichman D. R. Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function. Phys. Rev. E, 2020, 101(1), 010602. doi:10.1103/PHYSREVE.101.010602http://dx.doi.org/10.1103/PHYSREVE.101.010602

Le T.; Epa V. C.; Burden F. R.; Winkler D. A. Quantitative structure-property relationship modeling of diverse materials properties. Chem. Rev., 2012, 112(5), 2889-2919. doi:10.1021/cr200066hhttp://dx.doi.org/10.1021/cr200066h

Karuth A.; Alesadi A.; Xia W. J.; Rasulev B. Predicting glass transition of amorphous polymers by application of cheminformatics and molecular dynamics simulations. Polymer, 2021, 218, 123495. doi:10.1016/j.polymer.2021.123495http://dx.doi.org/10.1016/j.polymer.2021.123495

Higuchi C.; Horvath D.; Marcou G.; Yoshizawa K.; Varnek A. Prediction of the glass-transition temperatures of linear homo/heteropolymers and cross-linked epoxy resins. ACS Appl. Polym. Mater., 2019, 1(6), 1430-1442. doi:10.1021/acsapm.9b00198http://dx.doi.org/10.1021/acsapm.9b00198

Jin K.; Luo H.; Wang Z. Y.; Wang H.; Tao J. Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning. Mater. Des., 2020, 194, 108932. doi:10.1016/j.matdes.2020.108932http://dx.doi.org/10.1016/j.matdes.2020.108932

Zhang Y.; Xu X. J. Machine learning glass transition temperature of styrenic random copolymers. J. Mol. Graph. Model., 2021, 103, 107796. doi:10.1016/j.jmgm.2020.107796http://dx.doi.org/10.1016/j.jmgm.2020.107796

van Krevelen D. W.; Te Nijenhuis, K. Properties of Polymers. Amsterdam: Elsevier, 2009.

Yu X. L.; Wang X. Y.; Wang H. L.; Liu A. H.; Zhang C. L. Prediction of the glass transition temperatures of styrenic copolymers using a QSPR based on the DFT method. J. Mol. Struct. Theochem, 2006, 766(2-3), 113-117. doi:10.1016/j.theochem.2006.04.018http://dx.doi.org/10.1016/j.theochem.2006.04.018

Xu X. L.; Douglas J. F.; Xu W. S. Influence of side-chain length and relative rigidities of backbone and side chains on glass formation of branched polymers. Macromolecules, 2021, 54(13), 6327-6341. doi:10.1021/acs.macromol.1c00834http://dx.doi.org/10.1021/acs.macromol.1c00834

Alesadi A.; Cao Z. Q.; Li Z. F.; Zhang S.; Zhao H. Y.; Gu X. D.; Xia W. J. Machine learning prediction of glass transition temperature of conjugated polymers from chemical structure. Cell Rep. Phys. Sci., 2022, 3(6), 100911. doi:10.1016/j.xcrp.2022.100911http://dx.doi.org/10.1016/j.xcrp.2022.100911

Cypcar C. C.; Camelio P.; Lazzeri V.; Mathias L. J.; Waegell B. Prediction of the glass transition temperature of multicyclic and bulky substituted acrylate and methacrylate polymers using the energy, volume, mass (EVM) QSPR model. Macromolecules, 1996, 29(27), 8954-8959. doi:10.1021/ma961170shttp://dx.doi.org/10.1021/ma961170s

Camelio P.; Cypcar C. C.; Lazzeri V.; Waegell B. A novel approach toward the prediction of the glass transition temperature: Application of the EVM model, a designer QSPR equation for the prediction of acrylate and methacrylate polymers. J. Polym. Sci. A Polym. Chem., 1997, 35(13), 2579-2590. doi:10.1002/(sici)1099-0518(19970930)35:13<2579::aid-pola5>3.0.co;2-mhttp://dx.doi.org/10.1002/(sici)1099-0518(19970930)35:13<2579::aid-pola5>3.0.co;2-m

Carro A. M.; Campisi B.; Camelio P.; Phan-Tan-Luu R. Improving an EVM QSPR model for glass transition temperature prediction using optimal design. Chemom. Intell. Lab. Syst., 2002, 62(1), 79-88. doi:10.1016/s0169-7439(02)00002-3http://dx.doi.org/10.1016/s0169-7439(02)00002-3

Huan T. D.; Boggs S.; Teyssedre G.; Laurent C.; Cakmak M.; Kumar S.; Ramprasad R. Advanced polymeric dielectrics for high energy density applications. Prog. Mater. Sci., 2016, 83, 236-269. doi:10.1016/j.pmatsci.2016.05.001http://dx.doi.org/10.1016/j.pmatsci.2016.05.001

Ho J. S.; Greenbaum S. G. Polymer capacitor dielectrics for high temperature applications. ACS Appl. Mater. Interfaces, 2018, 10(35), 29189-29218. doi:10.1021/acsami.8b07705http://dx.doi.org/10.1021/acsami.8b07705

Wu C.; Deshmukh A. A.; Li Z. Z.; Chen L. H.; Alamri A.; Wang Y. F.; Ramprasad R.; Sotzing G. A.; Cao Y. Flexible temperature‐invariant polymer dielectrics with large bandgap. Adv. Mater., 2020, 32(21), 2000499. doi:10.1002/adma.202000499http://dx.doi.org/10.1002/adma.202000499

Kim C.; Batra R.; Chen L. H.; Tran H.; Ramprasad R. Polymer design using genetic algorithm and machine learning. Comput. Mater. Sci., 2021, 186, 110067. doi:10.1016/j.commatsci.2020.110067http://dx.doi.org/10.1016/j.commatsci.2020.110067

Batra R.; Dai H. J.; Huan T. D.; Chen L. H.; Kim C.; Gutekunst W. R.; Song L.; Ramprasad R. Polymers for extreme conditions designed using syntax-directed variational autoencoders. Chem. Mater., 2020, 32(24), 10489-10500. doi:10.1021/acs.chemmater.0c03332http://dx.doi.org/10.1021/acs.chemmater.0c03332

Palacín M. R. Recent advances in rechargeable battery materials: A chemist's perspective. Chem. Soc. Rev., 2009, 38(9), 2565-2575. doi:10.1039/b820555hhttp://dx.doi.org/10.1039/b820555h

Hatakeyama-Sato K.; Tezuka T.; Umeki M.; Oyaizu K. AI-assisted exploration of superionic glass-type Li+ conductors with aromatic structures. J. Am. Chem. Soc., 2020, 142(7), 3301-3305. doi:10.1021/jacs.9b11442http://dx.doi.org/10.1021/jacs.9b11442

Ediger M. D.; Angell C. A.; Nagel S. R. Supercooled liquids and glasses. J. Phys. Chem., 1996, 100(31), 13200-13212. doi:10.1021/jp953538dhttp://dx.doi.org/10.1021/jp953538d

Berthier L.; Biroli G. Theoretical perspective on the glass transition and amorphous materials. Rev. Mod. Phys., 2011, 83(2), 587-645. doi:10.1103/revmodphys.83.587http://dx.doi.org/10.1103/revmodphys.83.587

Bocquet L.; Colin A.; Ajdari A. Kinetic theory of plastic flow in soft glassy materials. Phys. Rev. Lett., 2009, 103(3), 036001. doi:10.1103/physrevlett.103.036001http://dx.doi.org/10.1103/physrevlett.103.036001

Cubuk E. D.; Schoenholz S. S.; Rieser J. M.; Malone B. D.; Rottler J.; Durian D. J.; Kaxiras E.; Liu A. J. Identifying structural flow defects in disordered solids using machine-learning methods. Phys. Rev. Lett., 2015, 114(10), 108001. doi:10.1103/physrevlett.114.108001http://dx.doi.org/10.1103/physrevlett.114.108001

Ma X. G.; Davidson Z. S.; Still T.; Ivancic R. J. S.; Schoenholz S. S.; Liu A. J.; Yodh A. G. Heterogeneous activation, local structure, and softness in supercooled colloidal liquids. Phys. Rev. Lett., 2019, 122(2), 028001. doi:10.1103/physrevlett.122.028001http://dx.doi.org/10.1103/physrevlett.122.028001

Cubuk E. D.; Ivancic R. J. S.; Schoenholz S. S.; Strickland D. J.; Basu A.; Davidson Z. S.; Fontaine J.; Hor J. L.; Huang Y. R.; Jiang Y.; Keim N. C.; Koshigan K. D.; Lefever J. A.; Liu T.; Ma X. G.; Magagnosc D. J.; Morrow E.; Ortiz C. P.; Rieser J. M.; Shavit A.; Still T.; Xu Y.; Zhang Y.; Nordstrom K. N.; Arratia P. E.; Carpick R. W.; Durian D. J.; Fakhraai Z.; Jerolmack D. J.; Lee D.; Li J.; Riggleman R.; Turner K. T.; Yodh A. G.; Gianola D. S.; Liu A. J. Structure-property relationships from universal signatures of plasticity in disordered solids. Science, 2017, 358(6366), 1033-1037. doi:10.1126/science.aai8830http://dx.doi.org/10.1126/science.aai8830

Zhang G.; Ridout S. A.; Liu A. J. Interplay of rearrangements, strain, and local structure during avalanche propagation. Phys. Rev. X, 2021, 11(4), 041019. doi:10.1103/physrevx.11.041019http://dx.doi.org/10.1103/physrevx.11.041019

Schoenholz S. S.; Cubuk E. D.; Sussman D. M.; Kaxiras E.; Liu A. J. A structural approach to relaxation in glassy liquids. Nat. Phys., 2016, 12(5), 469-471. doi:10.1038/nphys3644http://dx.doi.org/10.1038/nphys3644

Schmidtke B.; Hofmann M.; Lichtinger A.; Rössler E. A. Temperature dependence of the segmental relaxation time of polymers revisited. Macromolecules, 2015, 48(9), 3005-3013. doi:10.1021/acs.macromol.5b00204http://dx.doi.org/10.1021/acs.macromol.5b00204

Starr F. W.; Douglas J. F.; Sastry S. The relationship of dynamical heterogeneity to the Adam-Gibbs and random first-order transition theories of glass formation. J. Chem. Phys., 2013, 138(12), 12A541. doi:10.1063/1.4790138http://dx.doi.org/10.1063/1.4790138

Boattini E.; Dijkstra M.; Filion L. Unsupervised learning for local structure detection in colloidal systems. J. Chem. Phys., 2019, 151(15), 154901. doi:10.1063/1.5118867http://dx.doi.org/10.1063/1.5118867

Boattini E.; Marín-Aguilar S.; Mitra S.; Foffi G.; Smallenburg F.; Filion L. Autonomously revealing hidden local structures in supercooled liquids. Nat. Commun., 2020, 11(1), 5479. doi:10.1038/s41467-020-19286-8http://dx.doi.org/10.1038/s41467-020-19286-8

Boattini E.; Smallenburg F.; Filion L. Averaging local structure to predict the dynamic propensity in supercooled liquids. Phys. Rev. Lett., 2021, 127(8), 088007. doi:10.1103/physrevlett.127.088007http://dx.doi.org/10.1103/physrevlett.127.088007

Alkemade R. M.; Boattini E.; Filion L.; Smallenburg F. Comparing machine learning techniques for predicting glassy dynamics. J. Chem. Phys., 2022, 156(20), 204503. doi:10.1063/5.0088581http://dx.doi.org/10.1063/5.0088581

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. Unveiling the predictive power of static structure in glassy systems. Nat. Phys., 2020, 16(4), 448-454. doi:10.1038/s41567-020-0842-8http://dx.doi.org/10.1038/s41567-020-0842-8

Xia J. S.; Guo H. X. Construction of a quantitative relation between structural relaxation and dynamic heterogeneity by vibrational dynamics in glass-forming liquids and polymers. Soft Matter, 2021, 17(47), 10753-10764. doi:10.1039/d1sm01049bhttp://dx.doi.org/10.1039/d1sm01049b

Mckenzie-Smith T. Q.; Douglas J. F.; Starr F. W. Explaining the sensitivity of polymer segmental relaxation to additive size based on the localization model. Phys. Rev. Lett., 2021, 127(27), 277802. doi:10.1103/physrevlett.127.277802http://dx.doi.org/10.1103/physrevlett.127.277802

Douglas J. F.; Xu W. S. Equation of state and entropy theory approach to thermodynamic scaling in polymeric glass-forming liquids. Macromolecules, 2021, 54(7), 3247-3269. doi:10.1021/acs.macromol.1c00075http://dx.doi.org/10.1021/acs.macromol.1c00075

Berthier L.; Biroli G.; Charbonneau P.; Corwin E. I.; Franz S.; Zamponi F. Gardner physics in amorphous solids and beyond. J. Chem. Phys., 2019, 151(1), 010901. doi:10.1063/1.5097175http://dx.doi.org/10.1063/1.5097175

Li H. P.; Jin Y. L.; Jiang Y.; Chen J. Z. Y. Determining the nonequilibrium criticality of a Gardner transition via a hybrid study of molecular simulations and machine learning. Proc. Natl. Acad. Sci. U.S.A., 2021, 118(11), e2017392118. doi:10.1073/pnas.2017392118http://dx.doi.org/10.1073/pnas.2017392118

Zhang K. H.; Li X. Y.; Jin Y. L.; Jiang Y. Machine learning glass caging order parameters with an artificial nested neural network. Soft Matter, 2022, 18(33), 6270-6277. doi:10.1039/d2sm00310dhttp://dx.doi.org/10.1039/d2sm00310d

Xu X. L.; Xu W. S. Melt properties and string model description of glass formation in graft polymers of different side-chain lengths. Macromolecules, 2022, 55(8), 3221-3235. doi:10.1021/acs.macromol.2c00327http://dx.doi.org/10.1021/acs.macromol.2c00327

Drayer W. F.; Simmons D. S. Sequence effects on the glass transition of a model copolymer system. Macromolecules, 2022, 55(14), 5926-5937. doi:10.1021/acs.macromol.2c00664http://dx.doi.org/10.1021/acs.macromol.2c00664

Yang Z. Y.; Xu X. L.; Xu W. S. Influence of ionic interaction strength on glass formation of an ion-containing polymer melt. Macromolecules, 2021, 54(20), 9587-9601. doi:10.1021/acs.macromol.1c01719http://dx.doi.org/10.1021/acs.macromol.1c01719

胡文兵, 沈祥建, 高飞雪. 软物质非平衡体系的挑战和机遇. 高分子学报, 2023, 54(1), 65-77.

Yu K. Q.; Li Z. S.; Sun J. Z. Polymer structures and glass transition: a molecular dynamics simulation study. Macromol. Theory Simul., 2001, 10(6), 624-633. doi:10.1002/1521-3919(20010701)10:6<624::aid-mats624>3.0.co;2-khttp://dx.doi.org/10.1002/1521-3919(20010701)10:6<624::aid-mats624>3.0.co;2-k

Afzal M. A. F.; Browning A. R.; Goldberg A.; Halls M. D.; Gavartin J. L.; Morisato T.; Hughes T. F.; Giesen D. J.; Goose J. E. High-throughput molecular dynamics simulations and validation of thermophysical properties of polymers for various applications. ACS Appl. Polym. Mater., 2021, 3(2), 620-630. doi:10.1021/acsapm.0c00524http://dx.doi.org/10.1021/acsapm.0c00524

Odegard G. M.; Patil S. U.; Gaikwad P. S.; Deshpande P.; Krieg A. S.; Shah S. P.; Reyes A.; Dickens T.; King J. A.; Maiaru M. Accurate predictions of thermoset resin glass transition temperatures from all-atom molecular dynamics simulation. Soft Matter, 2022, 18(39), 7550-7558. doi:10.1039/d2sm00851chttp://dx.doi.org/10.1039/d2sm00851c

Massobrio C.; Du J. C.; Bernasconi M.; Salmon P. S. Molecular Dynamics Simulations of Disordered Materials. Cham: Springer International Publishing, 2015. doi:10.1007/978-3-319-15675-0http://dx.doi.org/10.1007/978-3-319-15675-0

Liu H.; Fu Z. P.; Li Y. P.; Sabri N. F. A.; Bauchy M. Machine Learning Forcefield for Silicate Glasses. 2019, arXiv: 1902.03486. doi:10.1557/mrc.2019.47http://dx.doi.org/10.1557/mrc.2019.47

Liu H.; Fu Z. P.; Li Y. P.; Sabri N. F. A.; Bauchy M. Balance between accuracy and simplicity in empirical forcefields for glass modeling: insights from machine learning. J. Non Cryst. Solids, 2019, 515, 133-142. doi:10.1016/j.jnoncrysol.2019.04.020http://dx.doi.org/10.1016/j.jnoncrysol.2019.04.020

Giuntoli A.; Hansoge N. K.; van Beek A.; Meng Z. X.; Chen W.; Keten S. Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization. Npj Comput. Mater., 2021, 7, 168. doi:10.1038/s41524-021-00634-1http://dx.doi.org/10.1038/s41524-021-00634-1

Deringer V. L.; Csányi G. Machine learning based interatomic potential for amorphous carbon. Phys. Rev. B, 2017, 95(9), 094203. doi:10.1103/physrevb.95.094203http://dx.doi.org/10.1103/physrevb.95.094203

Bartók A. P.; Kermode J.; Bernstein N.; Csányi G. Machine learning a general-purpose interatomic potential for silicon. Phys. Rev. X, 2018, 8(4), 041048. doi:10.1103/physrevx.8.041048http://dx.doi.org/10.1103/physrevx.8.041048

Rowe P.; Csányi G.; Alfè D.; Michaelides A. Development of a machine learning potential for graphene. Phys. Rev. B, 2018, 97(5), 054303. doi:10.1103/physrevb.97.054303http://dx.doi.org/10.1103/physrevb.97.054303

更多指标>

Views

188

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Advances and Challenges of Machine Learning in Polymer Material Genomes

Polymer Genome Approach: A New Method for Research and Development of Polymers

Rational Design, Tailored Superstructures, and Functionalities of Self-assembling Systems with Janus Particles: A Perspective of Theory and Simulation Studies

Related Author

No data

Related Institution

Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, North Dakota

School of Chemistry

Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of Education

Center of Soft Matter Physics and Its Applications

Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University

Postal code：100190
Tel：010-62588927 Email：gfzxb@iccas.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备05002797号-8 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰