The transformative capability of quantum-accurate machine learning interatomic potentials
arXiv:2506.02328 · doi:10.25950/ce4db4e8
Abstract
Many materials's 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, 1152 (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.
8 pages, 5 figures, KIM Review journal commentary paper on "Extreme Metastability of Diamond and its Transformation to the BC8 Post-Diamond Phase of Carbon" J.Phys.Chem.Lett. 15, 1152 (2024)