References

BMS+22

Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications, 13(1):2453, May 2022. URL: https://doi.org/10.1038/s41467-022-29939-5, doi:10.1038/s41467-022-29939-5.

BuvckoHAngyan05

Tomáš Bučko, Jürgen Hafner, and János G. Ángyán. Geometry optimization of periodic systems using internal coordinates. The Journal of Chemical Physics, 122(12):124508, mar 2005. URL: https://doi.org/10.1063/1.1864932, doi:10.1063/1.1864932.

CO22

Chi Chen and Shyue Ping Ong. A universal graph deep learning interatomic potential for the periodic table. 2022. arXiv:arXiv:2202.02450.

CYZ+19

Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong. Graph networks as a universal machine learning framework for molecules and crystals. Chemistry of Materials, 31(9):3564–3572, 2019. URL: https://doi.org/10.1021/acs.chemmater.9b01294, doi:10.1021/acs.chemmater.9b01294.

JKK19

Ryosuke Jinnouchi, Ferenc Karsai, and Georg Kresse. On-the-fly machine learning force field generation: application to melting points. Phys. Rev. B, 100:014105, Jul 2019. URL: https://link.aps.org/doi/10.1103/PhysRevB.100.014105, doi:10.1103/PhysRevB.100.014105.

KME19

Emir Kocer, Jeremy K. Mason, and Hakan Erturk. A novel approach to describe chemical environments in high-dimensional neural network potentials. The Journal of Chemical Physics, 150(15):154102, 2019. URL: https://doi.org/10.1063/1.5086167, doi:10.1063/1.5086167.

TPM09

Aidan P. Thompson, Steven J. Plimpton, and William Mattson. General formulation of pressure and stress tensor for arbitrary many-body interaction potentials under periodic boundary conditions. The Journal of Chemical Physics, 131(15):154107, 2009. URL: https://doi.org/10.1063/1.3245303, doi:10.1063/1.3245303.

ZCL+20

Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, and Shyue Ping Ong. Performance and cost assessment of machine learning interatomic potentials. The Journal of Physical Chemistry A, 124(4):731–745, 2020. PMID: 31916773. URL: https://doi.org/10.1021/acs.jpca.9b08723, doi:10.1021/acs.jpca.9b08723.

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