Physical insights into stress–strain process of polymers under tensile deformation via machine learning

References

• Kramer, E. J.; Berger, L. L. Crazing in Polymers. In H. -H. Kausch Ed., Advances in Polymer Science. Vol. 2. Springer: Berlin, Heidelberg, 1990; 1–68. DOI: 10.1007/BFb0018018
   Google Scholar
• Argon, A. S.; Hannoosh, J. G. Initiation of Crazes in Polystyrene. Philos. Mag. 1977, 36, 1195–1216. DOI: 10.1080/14786437708239789.
   Web of Science ®Google Scholar
• Chern, S. S.; Hsiao, C. C. A Generalized Time-dependent Theory on Craze Initiation in Viscoelastic Media. J. Appl. Phys. 1985, 57, 1823–1834. DOI: 10.1063/1.334410.
   Web of Science ®Google Scholar
• Rottler, J.; Robbins, M. O. Growth, Microstructure, and Failure of Crazes in Glassy Polymers. Phys. Rev. E. 2003, 68, 011801. DOI: 10.1103/PhysRevE.68.011801.
   PubMed Web of Science ®Google Scholar
• Toepperwein, G. N.; de Pablo, J. J. Cavitation and Crazing in Rod-Containing Nanocomposites. Macromolecules. 2011, 44, 5498–5509. DOI: 10.1021/ma200541s.
   Web of Science ®Google Scholar
• Ge, T.; Tzoumanekas, C.; Anogiannakis, S. D.; Hoy, R. S.; Robbins, M. O. Entanglements in Glassy Polymer Crazing: Cross-Links or Tubes? Macromolecules. 2017, 50, 459–471. DOI: 10.1021/acs.macromol.6b02125.
   Web of Science ®Google Scholar
• Rottler, J.; Robbins, M. O. Yield Conditions for Deformation of Amorphous Polymer Glasses. Phys. Rev. E. 2001, 64, 051801. DOI: 10.1103/PhysRevE.64.051801.
   Web of Science ®Google Scholar
• Ichinomiya, T.; Obayashi, I.; Hiraoka, Y. Persistent Homology Analysis of Craze Formation. Phys. Rev. E. 2017, 95, 012504. DOI: 10.1103/PhysRevE.95.012504.
   PubMed Web of Science ®Google Scholar
• Rottler, J.; Barsky, S.; Robbins, M. O. Cracks and Crazes: On Calculating the Macroscopic Fracture Energy of Glassy Polymers from Molecular Simulations. Phys. Rev. Lett. 2002, 89, 148304. DOI: 10.1103/PhysRevLett.89.148304.
   PubMed Web of Science ®Google Scholar
• Sixou, B.;. Molecular Dynamics Simulation of the First Stages of the Cavitation Process in Amorphous Polymers. Mol. Simul. 2007, 33, 965–973. DOI: 10.1080/08927020701502057.
   Web of Science ®Google Scholar
• Makke, A.; Perez, M.; Rottler, J.; Lame, O.; Barrat, J.-L. Predictors of Cavitation in Glassy Polymers under Tensile Strain: A Coarse-Grained Molecular Dynamics Investigation. Macromol. Theory Simul. 2011, 20, 826–836. DOI: 10.1002/mats.201100006.
   Web of Science ®Google Scholar
• Shi, R.; Qian, H.-J.; Lu, Z.-Y. Tuning Cavitation and Crazing in Polymer Nanocomposite Glasses Containing Bimodal Grafted Nanoparticles at the Nanoparticle/polymer Interface. Phys. Chem. Chem. Phys. 2019, 21, 7115–7126. DOI: 10.1039/C9CP00208A.
   PubMed Web of Science ®Google Scholar
• Shi, R.; Qian, H.-J.; Lu, Z.-Y. Interfacial Tuning of the Cavitation and Strain-Softening Behavior of Polymer/Nanoparticle Composites in the Glassy State. Macromolecules. 2019, 52, 7353–7360. DOI: 10.1021/acs.macromol.9b01312.
   Web of Science ®Google Scholar
• Schmidt, J.; Marques, M. R. G.; Botti, S.; Marques, M. A. L. Recent Advances and Applications of Machine Learning in Solid-state Materials Science. Npj Comput. Mater. 2019, 5, 83.
   Web of Science ®Google Scholar
• Liu, H.; Fu, Z.; Yang, K.; Xu, X.; Bauchy, M. Machine Learning for Glass Science and Engineering: A Review. J. Non·Cryst. Solids. 2019, 119419. doi:10.1016/j.jnoncrysol.2019.04.039.
   Web of Science ®Google Scholar
• Cubuk, E. D.; Schoenholz, S. S.; Rieser, J. M.; Malone, B. D.; Rottler, J.; Durian, D. J.; Kaxiras, E.; Liu, A. Defects in Disordered Solids Using Machine-Learning Methods. Phys. Rev. Lett. 2015, 114, 108001. DOI: 10.1103/PhysRevLett.114.108001.
   PubMed Web of Science ®Google Scholar
• Cubuk, E. D.; Schoenholz, S. S.; Kaxiras, E.; Liu, A. J. Structural Properties of Defects in Glassy Liquids. J. Phys. Chem. B. 2016, 120, 6139–6146. DOI: 10.1021/acs.jpcb.6b02144.
   PubMedGoogle Scholar
• 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, 469. DOI: 10.1038/nphys3644.
   Web of Science ®Google Scholar
• 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. Structure-property Relationships from Universal Signatures of Plasticity in Disordered Solids. Science. 2017, 358(6366), 1033.
   PubMed Web of Science ®Google Scholar
• 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, 263. DOI: 10.1073/pnas.1610204114.
   PubMed Web of Science ®Google Scholar
• Samuel, S. S.;. Combining Machine Learning and Physics to Understand Glassy Systems. J. Phys. Conf. Ser. 2018, 1036, 012021.
   Google Scholar
• Sharp, T. A.; Thomas, S. L.; Cubuk, E. D.; Schoenholz, S. S.; Srolovitz, D. J.; Liu, A. J. Machine Learning Determination of Atomic Dynamics at Grain Boundaries. Proc. Natl. Acad. Sci. U.S.A. 2018, 115, 10943. DOI: 10.1073/pnas.1807176115.
   PubMed Web of Science ®Google Scholar
• Harrington, M.; Liu, A. J.; Durian, D. J. Machine Learning Characterization of Structural Defects in Amorphous Packings of Dimers and Ellipses. Phys. Rev. E. 2019, 99, 022903. DOI: 10.1103/PhysRevE.99.022903.
   PubMed Web of Science ®Google Scholar
• Ivancic, R. J. S.; Riggleman, R. A. Identifying Structural Signatures of Shear Banding in Model Polymer Nanopillars. Soft Matter. 2019, 15, 4548–4561. DOI: 10.1039/C8SM02423E.
   PubMed Web of Science ®Google Scholar
• Ma, X.; 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, 028001. DOI: 10.1103/PhysRevLett.122.028001.
   PubMed Web of Science ®Google Scholar
• 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, 10601–10605. DOI: 10.1073/pnas.1703927114.
   PubMed Web of Science ®Google Scholar
• Jackson, N. E.; Bowen, A. S.; Antony, L. W.; Webb, M. A.; Vishwanath, V.; de Pablo, J. J. Electronic Structure at Coarse-grained Resolutions from Supervised Machine Learning. Science Advances. 2019, 5, eaav1190. DOI: 10.1126/sciadv.aav1190.
   PubMed Web of Science ®Google Scholar
• Wang, J.; Olsson, S.; Wehmeyer, C.; Pérez, A.; Charron, N. E.; de Fabritiis, G.; Noé, F.; Clementi, C. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS Cent. Sci. 2019, 5, 755–767. DOI: 10.1021/acscentsci.8b00913.
   PubMed Web of Science ®Google Scholar
• Labouta, H. I.; Asgarian, N.; Rinker, K.; Cramb, D. T. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. ACS Nano. 2019, 13, 1583–1594. DOI: 10.1021/acsnano.8b07562.
   PubMed Web of Science ®Google Scholar
• Kremer, K.; Grest, G. S. Dynamics of Entangled Linear Polymer Melts: A Molecular‐dynamics Simulation. J. Chem. Phys. 1990, 92, 5057–5086. DOI: 10.1063/1.458541.
   Web of Science ®Google Scholar
• Zhu, Y.-L.; Liu, H.; Li, Z.-W.; Qian, H.-J.; Milano, G.Lu, Z.-Y. GALAMOST: GPU-accelerated Large-scale Molecular Simulation Toolkit, Journal Of Computational Chemistry, 2013, 34, 2197. DOI: 10.1002/jcc.23365 25
   PubMed Web of Science ®Google Scholar
• Zhu, Y.-L.; Pan, D.; Li, Z.-W.; Liu, H.; Qian, H.-J.; Zhao, Y.; Lu, Z.-Y.Sun, Z.-Y. Employing Multi-GPU Power for Molecular Dynamics Simulation: an Extension Of Galamost. Molecular Physics, 2018, 116, 1065. DOI: 10.1080/00268976.2018.1434904 7–8
   Web of Science ®Google Scholar
• Papakonstantopoulos, G. J.; Riggleman, R. A.; Barrat, J.-L.; de Pablo, J. J. Molecular Plasticity of Polymeric Glasses in the Elastic Regime. Phys. Rev. E. 2008, 77, 041502. DOI: 10.1103/PhysRevE.77.041502.
   Web of Science ®Google Scholar
• Lutsko, J. F. Stress and Elastic Constants in Anisotropic Solids: Molecular Dynamics Techniques. J. Appl. Phys. 1988, 64, 1152–1154. DOI: 10.1063/1.341877.
   Web of Science ®Google Scholar
• Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V; Vanderplas, J. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830.
   Web of Science ®Google Scholar