Machine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions

Abstract

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.

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Cited by in corpus (6)

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• High Pressure and Temperature Neural Network Reactive Force Field for Energetic Materials
• Properties of carbon up to 10 million kelvin from Kohn-Sham density functional theory molecular dynamics
• Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter
• Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon
• From diamond to BC8 to simple cubic and back: kinetic pathways to post-diamond carbon phases from metadynamics