Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Volker L. Deringer*, Miguel A. Caro, Gábor Csányi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

83 Citations (Scopus)
219 Downloads (Pure)


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
Number of pages16
JournalAdvanced Materials
Issue number46
Early online date5 Sep 2019
Publication statusPublished - Nov 2019
MoE publication typeA1 Journal article-refereed


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


Dive into the research topics of 'Machine Learning Interatomic Potentials as Emerging Tools for Materials Science'. Together they form a unique fingerprint.

Cite this