Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • University of Cambridge


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.


Original languageEnglish
Article number1902765
JournalAdvanced Materials
Publication statusPublished - 5 Sep 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Amorphous solids, Atomistic modeling, Big data, Force fields, Molecular dynamics

ID: 37066885