When do short-range atomistic machine-learning models fall short?

Shuwen Yue, Maria Carolina Muniz, Marcos F.Calegari Andrade, Linfeng Zhang, Roberto Car, Athanassios Z. Panagiotopoulos

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.

Original languageEnglish (US)
Article number034111
JournalJournal of Chemical Physics
Volume154
Issue number3
DOIs
StatePublished - Jan 21 2021

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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