Quantum-accurate large-scale atomistic simulation of materials with LAMMPS and FitSNAP

Molecular dynamics (MD) is a powerful materials simulation approach whose accuracy is limited by the interatomic potential (IAP). The quest for improved accuracy has resulted in a decades-long growth in the complexity of IAPs, many of which are now implemented in the LAMMPS MD code[1]. Traditional physics-based IAPs are now being rapidly supplanted by machine-learning IAPs. The SNAP (Spectral Neighbor Analysis Potential) machine-learning approach is one example of this[2]. SNAP is formulated in terms of a set of general four-body descriptors that characterize the local neighborhood of each atom. The FitSNAP software[3], tightly integrated with LAMMPS, provides an automated methodology for generating accurate and robust application-specific IAPs. This approach has been used to develop potentials for diverse materials, including metal alloys, semiconductors, plasma-facing materials, and even magnetic materials such as iron. Each SNAP IAP is trained on quantum electronic structure calculations of energy, force, and stress for many small configurations of atoms. The resultant potentials enable high-fidelity large-scale MD simulations of these materials, yielding insight into their behavior on lengthscales and timescales unreachable by other methods. The relatively large computational cost of SNAP is offset by combining LAMMPS' spatial parallel algorithms with Kokkos-based hierarchical multithreading, enabling the efficient use of Exa-scale CPU and GPU platforms, allowing large-scale production simulations at 30 ns/day with millions to billions of atoms. Finally, I will discuss extensions of FitSNAP and LAMMPS to handle other ML styles, including neural network libraries and the more general Atomic Cluster Expansion descriptors.