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The transformative capability of quantum-accurate machine learning interatomic potentials

Alfredo A. Correa¹; Sebastien Hamel^1
1Quantum Simulations Group, Physics Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA

Commentary on

K. Nguyen-Cong, J. T. Willman, J. M. Gonzalez, A. S. Williams, A. B. Belonoshko, S. G. Moore, A. P. Thompson, M. A. Wood, J. H. Eggert, M. Millot, L. A. Zepeda-Ruiz and I. I. Oleynik, “Extreme metastability of diamond and its transformation to the BC8 post-diamond phase of carbon”, J. Phys. Chem. Lett., 15(4):1152-1160 (2024), https://doi.org/10.1021/acs.jpclett.3c03044

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Statement of Significance

Many materials' properties and phase boundaries are generally not well known under extreme pressure and temperature conditions. This is a consequence of the scarcity of experimental information and the difficulty of extrapolating approximations to the atomic interactions in such conditions. Nguyen-Cong and colleagues, in their publication (J.Phys.Chem.Lett., 15(4):1152-1160 (2024)), achieved an impressive result using a SNAP (Spectral Neighbor Analysis Potential), an interatomic potential for carbon obtained by machine learning techniques. In a way, their contribution closes a full circle of research that spanned more than three decades.

How to cite

Cite the commentary as:

A. A. Correa & S. Hamel, "The transformative capability of quantum-accurate machine learning interatomic potentials",
KIM REVIEW, Volume 3, Article 01, 2025. DOI: 10.25950/ce4db4e8