(659f) Development of Physics-Informed Neural Network Potentials for Molecular Simulations
- Conference: AIChE Annual Meeting
- Year: 2019
- Proceeding: 2019 AIChE Annual Meeting
- Group: Computational Molecular Science and Engineering Forum
- Session:
- Time: Thursday, November 14, 2019 - 9:30am-9:45am
References:
[1] Jones, J. E. (1924). On the determination of molecular fields.âII. From the equation of state of a gas. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 106(738), 463-477.
[2] Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S. A., & Karplus, M. (1983). CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. Journal of computational chemistry, 4(2), 187-217.
[3] Tersoff, J. (1988). New empirical approach for the structure and energy of covalent systems. Physical Review B, 37(12), 6991.
[4] Behler, J., & Parrinello, M. (2007). Generalized neural-network representation of high-dimensional potential-energy surfaces. Physical review letters, 98(14), 146401.
[5] Pun, G. P., Batra, R., Ramprasad, R., & Mishin, Y. (2018). Physically-informed artificial neural networks for atomistic modeling of materials. arXiv preprint arXiv:1808.01696.