[1] S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. R. Müller, and A. Tkatchenko, "Machine learning of accurate energy-conserving molecular force fields,"
Science Advances, vol. 4, no. 1, eaao5751, 2018, doi: 10.1126/sciadv.1603015.
[2] T. Xie and J. C. Grossman, "Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties," Physical Review Letters, vol. 120, no. 14, p. 145301, 2018, doi: 10.1103/PhysRevLett.120.145301.
[3] U. Unke, O. T., and M. Meuwly, "PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges," Journal of Chemical Theory and Computation, vol. 15, no. 6, pp. 3678-3693, Jun. 2019.
[4] W. Nash, T. Drummond, and N. Birbilis, "A review of deep learning in the study of materials degradation," npj Materials Degradation, vol. 2, no. 1, p. 37, 2018.
[5] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, "Neural message passing for quantum chemistry," International Conference on Machine Learning, 2017, pp. 1263-1272.
[6] K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, and K. R. Müller, "SchNetPack: A deep learning toolbox for atomistic systems," Journal of Chemical Theory and Computation, vol. 15, no. 1, pp. 448-455, 2018.
[7] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics," Journal of Computational Physics, vol. 117, no. 1, pp. 1-19, 1995.
[8] T. P. Senftle, S. Hong, M. M. Islam, S. B. Kylasa, Y. Zheng, Y. K. Shin, and A. C. Van Duin, "The ReaxFF reactive force-field: development, applications and future directions," npj Computational Materials, vol. 2, no. 1, pp. 1-14, 2016.
[9] G. Kresse and J. Furthmüller, "Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set," Computational Materials Science, vol. 6, no. 1, pp. 15-50, 1996.
[10] A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, "Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons," Physical Review Letters, vol. 104, no. 13, p. 136403, 2010.
[11] J. S. Smith, O. Isayev, and A. E. Roitberg, "ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost," Chemical Science, vol. 8, no. 4, pp. 3192-3203, 2017.
[12] J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, and M. Sun, "Graph neural networks: A review of methods and applications," AI Open, vol. 1, pp. 57-81, 2020.
[13] A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, and K. A. Persson, "Commentary: The Materials Project: A materials genome approach to accelerating materials innovation," APL Materials, vol. 1, no. 1, 2013.
