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The difficult problem of relating the static structure of glassy liquids and their dynamics is a good target for Machine Learning, an approach which excels at finding complex patterns hidden in data.
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L. Berthier and R. L. Jack, Structure and dynamics in glass-formers: predictability at large length scales, Physical Review E 76
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L. Berthier, G. Biroli, J.-P. Bouchaud, W. Kob, K. Miyazaki, and D. R. Reichman, Spontaneous and induced dynamic fluctuations in glass formers. I. General results and dependence on ensemble and dynamics, The Journal of Chemical Physics 126
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L. Berthier, G. Biroli, J.-P. Bouchaud, L. Cipelletti, and W. van Saarloos, Dynamical heterogeneities in glasses, colloids, and granular media , Vol. 150 (OUP Oxford, 2011)
2011
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P. Ronhovde, S. Chakrabarty, D. Hu, M. Sahu, K. K. Sahu, K. F. Kelton, N. A. Mauro, and Z. Nussinov, Detection of hidden structures for arbitrary scales in complex physical systems, Scientific reports 2
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Y. Bengio, Deep learning of representations for unsupervised and transfer learning, in Proceedings of ICML workshop on unsupervised and transfer learning (JMLR Workshop and Conference Proceedings, 2012) pp. 17–36
2012
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J. Rottler, S. S. Schoenholz, and A. J. Liu, Predicting plasticity with soft vibrational modes: From dislocations to glasses, Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 89
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S. S. Schoenholz, A. J. Liu, R. A. Riggleman, and J. Rottler, Understanding plastic deformation in thermal glasses from single-soft-spot dynamics, Physical Review X 4
2014
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E. D. Cubuk, S. S. Schoenholz, J. M. Rieser, B. D. Malone, J. Rottler, D. J. Durian, E. Kaxiras, and A. J. Liu, Identifying structural flow defects in disordered solids using machine-learning methods, Physical Review Letters 114
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S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in International conference on machine learning (pmlr, 2015) pp. 448–456
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S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley, Molecular graph convolutions: moving beyond fingerprints, Journal of Computer-Aided Molecular Design 30
2016
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2016
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C. P. Royall and W. Kob, Locally favoured structures and dynamic length scales in a simple glass-former, Journal of Statistical Mechanics: Theory and Experiment 2017
2017
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F. Turci, G. Tarjus, and C. P. Royall, From Glass Formation to Icosahedral Ordering by Curving Three-Dimensional Space, Physical Review Letters 118
2017
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E. D. Cubuk, R. J. S. Ivancic, S. S. Schoenholz, D. J. Strickland, A. Basu, Z. S. Davidson, J. Fontaine, J. L. Hor, Y.-R. Huang, Y. Jiang, N. C. Keim, K. D. Koshigan, J. A. Lefever, T. Liu, X.-G. Ma, D. J. Magagnosc, E. Morrow, C. P. Ortiz, J. M. Rieser, A. Shavit, T. Still, Y. Xu, Y. Zhang, K. N. Nordstrom, P. E. Arratia, R. W. Carpick, D. J. Durian, Z. Fakhraai, D. J. Jerolmack, D. Lee, J. Li, R. Riggleman, K. T. Turner, A. G. Yodh, D. S. Gianola, and A. J. Liu, Structure-property relationships from universal signatures of plasticity in disordered solids, Science 358
2017
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D. M. Sussman, S. S. Schoenholz, E. D. Cubuk, and A. J. Liu, Disconnecting structure and dynamics in glassy thin films, Proceedings of the National Academy of Sciences 114
2017
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H. Tong and H. Tanaka, Revealing Hidden Structural Order Controlling Both Fast and Slow Glassy Dynamics in Supercooled Liquids, Physical Review X 8
2018
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E. Boattini, F. Smallenburg, and L. Filion, Averaging local structure to predict the dynamic propensity in supercooled liquids, Physical Review Letters 127
2021
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J. Brandstetter, R. Hesselink, E. van der Pol, E. J. Bekkers, and M. Welling, Geometric and physical quantities improve e (3) equivariant message passing, in International Conference on Learning Representations (2021)
2021
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M. J. Hutchinson, C. Le Lan, S. Zaidi, E. Dupont, Y. W. Teh, and H. Kim, Lietransformer: Equivariant self-attention for lie groups, in International Conference on Machine Learning (PMLR, 2021) pp. 4533–4543
2021
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S. Liu, H. Wang, W. Liu, J. Lasenby, H. Guo, and J. Tang, Pre-training molecular graph representation with 3d geometry, in International Conference on Learning Representations (2021)
2021
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T. A. Sharp, S. L. Thomas, E. D. Cubuk, S. S. Schoenholz, D. J. Srolovitz, and A. J. Liu, Machine learning determination of atomic dynamics at grain boundaries, Proceedings of the National Academy of Sciences 115
2018
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2018
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M. Weiler, M. Geiger, M. Welling, W. Boomsma, and T. S. Cohen, 3d steerable cnns: Learning rotationally equivariant features in volumetric data, Advances in Neural Information Processing Systems 31
2018
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R. Kondor, Z. Lin, and S. Trivedi, Clebsch–gordan nets: a fully fourier space spherical convolutional neural network, Advances in Neural Information Processing Systems 31
2018
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E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville, Film: Visual reasoning with a general conditioning layer, in Proceedings of the AAAI conference on artificial intelligence , Vol. 32 (2018)
2018
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H. Tong and H. Tanaka, Structural order as a genuine control parameter of dynamics in simple glass formers, Nature Communications 10
2019
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H. Tanaka, H. Tong, R. Shi, and J. Russo, Revealing key structural features hidden in liquids and glasses, Nature Reviews Physics 1
2019
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C. Chen, W. Ye, Y. Zuo, C. Zheng, and S. P. Ong, Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals, Chemistry of Materials 31
2019
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M. Fey and J. E. Lenssen, Fast graph representation learning with PyTorch Geometric, in ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)
2019
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2021
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M. Lerbinger, A. Barbot, D. Vandembroucq, and S. Patinet, Relevance of shear transformations in the relaxation of supercooled liquids, Physical Review Letters 129
2022
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D. Coslovich, R. L. Jack, and J. Paret, Dimensionality reduction of local structure in glassy binary mixtures, The Journal of Chemical Physics 157
2022
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2022
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I. Tah, S. A. Ridout, and A. J. Liu, Fragility in glassy liquids: A structural approach based on machine learning, The Journal of Chemical Physics 157
2022
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I. Batatia, D. P. Kovacs, G. Simm, C. Ortner, and G. Csányi, Mace: Higher order equivariant message passing neural networks for fast and accurate force fields, Advances in Neural Information Processing Systems 35
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M. Geiger, T. Smidt, A. M., B. K. Miller, W. Boomsma, B. Dice, K. Lapchevskyi, M. Weiler, M. Tyszkiewicz, S. Batzner, D. Madisetti, M. Uhrin, J. Frellsen, N. Jung, S. Sanborn, M. Wen, J. Rackers, M. Rød, and M. Bailey, Euclidean neural networks: e3nn 10.5281/zenodo.6459381 (2022)
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Y.-L. Liao and T. Smidt, Equiformer: Equivariant graph attention transformer for 3d atomistic graphs, in The Eleventh International Conference on Learning Representations (2022)
2022
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C. Scalliet, B. Guiselin, and L. Berthier, Thirty milliseconds in the life of a supercooled liquid, Physical Review X 12
2022
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X. Fang, L. Liu, J. Lei, D. He, S. Zhang, J. Zhou, F. Wang, H. Wu, and H. Wang, Geometry-enhanced molecular representation learning for property prediction, Nature Machine Intelligence 4
2022
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H. Shiba, M. Hanai, T. Suzumura, and T. Shimokawabe, Botan: Bond targeting network for prediction of slow glassy dynamics by machine learning relative motion, The Journal of Chemical Physics 158
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N. Oyama, S. Koyama, and T. Kawasaki, What do deep neural networks find in disordered structures of glasses?, Frontiers in Physics 10
2023
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