A data-driven method for optimization of classical interatomic potentials
Author:
Funder
National Science Foundation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1557/s43580-024-00802-7.pdf
Reference27 articles.
1. J.A. Martinez, D.E. Yilmaz, T. Liang, S.B. Sinnott, S.R. Phillpot, Fitting empirical potentials: Challenges and methodologies. Curr. Opin. Solid State Mater. Sci. 17(6), 263–270 (2023). https://doi.org/10.1016/j.cossms.2013.09.001
2. F. Ercolessi, J.B. Adams, Interatomic potentials from first-principles calculations: The force-matching method. Europhys. Lett. (EPL) 26(8), 583–588 (2022). https://doi.org/10.1209/0295-5075/26/8/005
3. P. Brommer, A. Kiselev, D. Schopf, P. Beck, J. Roth, H.-R. Trebin, Classical interaction potentials for diverse materials from ab initio data: a review of potfit. Modell. Simul. Mater. Sci. Eng. 23(7), 074002 (2023). https://doi.org/10.1088/0965-0393/23/7/074002
4. R. Kobayashi, Nap: a molecular dynamics package with parameter-optimization programs for classical and machine-learning potentials. J. Open Source Softw. 6(57), 2768 (2023)
5. M. Wen, Y. Afshar, R.S. Elliott, E.B. Tadmor, KLIFF: A framework to develop physics-based and machine learning interatomic potentials. Comput. Phys. Commun. 272, 108218 (2023). https://doi.org/10.1016/j.cpc.2021.108218
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