  A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential
(MLIP) has been developed for simulations of carbon at extreme pressures (up to
5 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.
