M. Waskom, O. Botvinnik, D. O’Kane, P. Hobson, S. Lukauskas, D. C. Gemperline, T. Augspurger, Y. Halchenko, J. B. Cole, J. Warmenhoven, J. de Ruiter, C. Pye, S. Hoyer, J. Vanderplas, S. Villalba, G. Kunter, E. Quintero, P. Bachant, M. Martin, K. Meyer, A. Miles, Y. Ram, T. Yarkoni, M. L. Williams, C. Evans, C. Fitzgerald, Brian, C. Fonnesbeck, A. Lee, and A. Qalieh, “mwaskom/seaborn: v0.8.1 (september 2017),” (2017)
2017
Later among the works it cites.
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al. , “Relational inductive biases, deep learning, and graph networks,” arXiv preprint arXiv:1806.01261 (2018)
Original
2018
Later among the works it cites.
E. N. Feinberg, D. Sur, Z. Wu, B. E. Husic, H. Mai, Y. Li, S. Sun, J. Yang, B. Ramsundar, and V. S. Pande, “Potentialnet for molecular property prediction,” ACS Cent. Sci. 4
2018
Later among the works it cites.
T. Xie and J. C. Grossman, “Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,” Phys. Rev. Lett. 120
2018
Later among the works it cites.
L. Boninsegna, R. Banisch, and C. Clementi, “A data-driven perspective on the hierarchical assembly of molecular structures,” J. Chem. Theory Comput. 14
2018
Later among the works it cites.
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, “SchNet–a deep learning architecture for molecules and materials,” J. Chem. Phys. 148
2018
Later among the works it cites.
M. Guenza, M. Dinpajooh, J. McCarty, and I. Lyubimov, “Accuracy, transferability, and efficiency of coarse-grained models of molecular liquids,” J. Phys. Chem. B 122
2018
Later among the works it cites.
J. S. Smith, B. Nebgen, N. Lubbers, O. Isayev, and A. E. Roitberg, “Less is more: Sampling chemical space with active learning,” J. Chem. Phys. 148
2018
Later among the works it cites.
A. Grisafi, D. M. Wilkins, G. Csányi, and M. Ceriotti, “Symmetry-adapted machine learning for tensorial properties of atomistic systems,” Phys. Rev. Lett. 120
2018
Later among the works it cites.
G. Imbalzano, A. Anelli, D. Giofré, S. Klees, J. Behler, and M. Ceriotti, “Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials,” J. Chem. Phys. 148
2018
Later among the works it cites.
T. T. Nguyen, E. Székely, G. Imbalzano, J. Behler, G. Csányi, M. Ceriotti, A. W. Götz, and F. Paesani, “Comparison of permutationally invariant polynomials, neural networks, and gaussian approximation potentials in representing water interactions through many-body expansions,” J. Chem. Phys. 148
2018
Later among the works it cites.
L. Zhang, J. Han, H. Wang, W. Saidi, R. Car, and W. E, “End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems,” in Advances in Neural Information Processing Systems 31 , edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Curran Associates, Inc., 2018) pp. 4436–4446
2018
Later among the works it cites.
T. Bereau, R. A. DiStasio Jr, A. Tkatchenko, and O. A. Von Lilienfeld, “Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning,” J. Chem. Phys. 148
2018
Later among the works it cites.
H. Wang and W. Yang, “Toward building protein force fields by residue-based systematic molecular fragmentation and neural network,” J. Chem. Phys. 15
2018
Later among the works it cites.
J. Fass, D. A. Sivak, G. E. Crooks, K. A. Beauchamp, B. Leimkuhler, and J. D. Chodera, “Quantifying configuration-sampling error in langevin simulations of complex molecular systems,” Entropy 20
2018
Later among the works it cites.
M. M. Sultan and V. S. Pande, “Automated design of collective variables using supervised machine learning,” J. Chem. Phys. 149
2018
Later among the works it cites.
B. E. Husic and V. S. Pande, “Markov state models: From an art to a science,” J. Am. Chem. Soc. 140
2018
Later among the works it cites.
H. Gouk, E. Frank, B. Pfahringer, and M. Cree, “Regularisation of neural networks by enforcing Lipschitz continuity,” arXiv preprint arXiv:1804.04368 (2018)
Original
2018
Later among the works it cites.
K. Schutt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, and K.-R. Müller, “SchNetPack: A deep learning toolbox for atomistic systems,” J. Chem. Theory Comput. 15
2018
Later among the works it cites.
C. Wehmeyer, M. K. Scherer, T. Hempel, B. E. Husic, S. Olsson, and F. Noé, “Introduction to Markov state modeling with the PyEMMA software—v1. 0,” LiveCoMS 1
2018
Later among the works it cites.
W. Wang and R. Gómez-Bombarelli, “Coarse-graining auto-encoders for molecular dynamics,” npj Comput. Mater. 5
2019
Later among the works it cites.
J. Wang, S. Olsson, C. Wehmeyer, A. Pérez, N. E. Charron, G. De Fabritiis, F. Noé, and C. Clementi, “Machine learning of coarse-grained molecular dynamics force fields,” ACS Cent. Sci. 5
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 , edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché Buc, E. Fox, and R. Garnett (Curran Associates, Inc., 2019) pp. 8024–8035
2019
Later among the works it cites.
M. K. Scherer, B. E. Husic, M. Hoffmann, F. Paul, H. Wu, and F. Noé, “Variational selection of features for molecular kinetics,” J. Chem. Phys. 150
2019
Later among the works it cites.
F. Nüske, L. Boninsegna, and C. Clementi, “Coarse-graining molecular systems by spectral matching,” J. Chem. Phys. 151
2019
Later among the works it cites.
F. Noé, A. Tkatchenko, K.-R. Müller, and C. Clementi, “Machine learning for molecular simulation,” Annu. Rev. Phys. Chem 71
2020
Closest in time.
R. Gómez-Bombarelli and A. Aspuru-Guzik, “Machine learning and big-data in computational chemistry,” Handbook of Materials Modeling: Methods: Theory and Modeling , 1939–1962 (2020)
2020
Closest in time.
J. Ruza, W. Wang, D. Schwalbe-Koda, S. Axelrod, W. H. Harris, and R. Gomez-Bombarelli, “Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks,” arXiv preprint arXiv:2007.14144 (2020)
Original
2020
Closest in time.
J. Wang, S. Chmiela, K.-R. Müller, F. Noé, and C. Clementi, “Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach,” J. Chem. Phys. 152
2020
Closest in time.
F. Noé, “Machine learning for molecular dynamics on long timescales,” in Machine Learning Meets Quantum Physics (Springer, Cambridge, 2020) pp. 331–372
2020
Closest in time.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature 585
2020
Closest in time.