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Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors—atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors—using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

We discuss the relative complexity and computational cost of several popular many-body empirical potentials, developed by the materials science community over the past 30 years. The inclusion of more detailed many-body effects has come at a computational cost, but the cost still scales linearly with the number of atoms modeled. This is enabling very large molecular dynamics simulations with unprecedented atomic-scale fidelity to physical and chemical phenomena. The cost and scalability of the potentials, run in serial and parallel, are benchmarked in the LAMMPS molecular dynamics code. Several recent large calculations performed with these potentials are highlighted to illustrate what is now possible on current supercomputers. We conclude with a brief mention of high-performance computing architecture trends and the research issues they raise for continued potential development and use.

Abstract

Several letters to the editor are presented in response to articles in previous issues including the letter from Roger Gray and Andy Standingarticle in the November issue, showcasing a version of an Edwardiantea caddy and seeking suggestion to find hardwood in Lurgan, Northern Ireland.