Quantum-accurate SNAP carbon potential for MD shock simulations

The ability of molecular dynamics (MD) to realistically simulate the high-strain-rate physics is critically dependent on the availability of high fidelity interatomic potentials capable of capturing the major physics of materials response to high temperatures and pressures. We have developed a Spectral Neighbor Analysis Potential (SNAP) machine-learning potential for high-pressure carbon. SNAP is formulated in terms of the bispectrum components, a set of general four-body geometric invariants that characterize the local neighborhood of each atom. Statistical data analysis is used to train the SNAP potential to reproduce a large set of first-principles training data. In this presentation we describe (1) the generation of the training database comprising the consistent and meaningful set of first-principles DFT calculations; (2) the robust and physically guided fit of the SNAP parameters; and (3) the validation of the SNAP potential in large-scale MD simulations of shock compression of carbon materials.