Machine learning interatomic potential for simulations of carbon at extreme conditions
Jonathan Willman, Kien Nguyen-Cong, Ashley Williams, A. B. Belonoshko, Stan Moore, Aidan P. Thompson, Mitchell Wood, Ivan Oleynik
Abstract
A spectral neighbor analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to $5\phantom{\rule{0.16em}{0ex}}\mathrm{TPa}$) and temperatures (up to 20 000 K). This was achieved using a large database of experimentally relevant quantum molecular dynamics (QMD) data, training the SNAP potential using a robust machine learning methodology, and performing extensive validation against QMD and experimental data. The resultant carbon MLIP demonstrates unprecedented accuracy and transferability in predicting the carbon phase diagram, melting curves of crystalline phases, and the shock Hugoniot, all within 3% of QMD. By achieving quantum accuracy and efficient implementation on leadership-class high-performance computing systems, SNAP advances frontiers of classical MD simulations by enabling atomic-scale insights at experimental time and length scales.