Physics

Nanotechnology

Electrode

Work (physics)

Interatomic potential

Density functional theory

Computational chemistry

Nanoparticle

Chemistry

Materials science

Condensed matter physics

Scale (ratio)

Computer science

Molecular dynamics

Quantum mechanics

Electrochemistry

Supercapacitor

Atomic units

Electronic structure

Thermodynamics

Advanced Materials

Atomic‐scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic‐structure methods such as density‐functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by “learning” electronic‐structure data, ML‐based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase‐change materials for memory devices; nanoparticle catalysts; and carbon‐based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML‐based interatomic potentials in diverse areas of materials research.

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science