Perspective: Machine learning potentials for atomistic simulations

TLDR

Computer simulations are essential across chemistry, physics, and materials science, yet realistic large‑scale modeling is limited by the scarcity of efficient, accurate interatomic potentials; recent machine‑learning approaches promise to overcome this bottleneck. The article aims to review the emerging paradigm shift toward machine‑learning based interatomic potentials. The authors review the core concepts, solved problems, and remaining challenges of ML potentials, discussing their applicability and limitations.

Abstract

Nowadays, computer simulations have become a standard tool in essentially all fields of chemistry, condensed matter physics, and materials science. In order to keep up with state-of-the-art experiments and the ever growing complexity of the investigated problems, there is a constantly increasing need for simulations of more realistic, i.e., larger, model systems with improved accuracy. In many cases, the availability of sufficiently efficient interatomic potentials providing reliable energies and forces has become a serious bottleneck for performing these simulations. To address this problem, currently a paradigm change is taking place in the development of interatomic potentials. Since the early days of computer simulations simplified potentials have been derived using physical approximations whenever the direct application of electronic structure methods has been too demanding. Recent advances in machine learning (ML) now offer an alternative approach for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations. In this perspective, the central ideas underlying these ML potentials, solved problems and remaining challenges are reviewed along with a discussion of their current applicability and limitations.