Quantum Accurate SNAP Carbon Potential for MD Shock Simulations

JT Willman and AS Williams and K Nguyen-Cong and AP Thompson and MA Wood and AB Belonoshko and II Oleynik, SHOCK COMPRESSION OF CONDENSED MATTER - 2019, 2272, 070055 (2020).

DOI: 10.1063/12.0000881

We present a new quantum accurate Spectral Neighbor Analysis Potential (SNAP) machine-learning potential for simulating carbon under extreme conditions of dynamic compression (pressures up to 1 TPa and temperatures up to 10,000 K). The development of SNAP potential involves (1) the generation of the training database comprised of the consistent and meaningful set of first-principles DFT (Density Functional Theory) data for carbon materials at high pressure and temperature; (2) the robust and physically guided training of the SNAP parameters on first- principles data involving statistical data analysis; and (3) the validation of the SNAP potential in MD simulations of carbon at high PT conditions. The excellent performance of quadratic SNAP potential is demonstrated by simulating the radial distribution functions at high pressure-temperature conditions and melt curve of diamond, which were found in good agreement with DFT.

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