Machine-learned acceleration for molecular dynamics in CASTEP

Tamás Stenczel, Zakariya El-Machachi, Guoda Liepuoniute, Joe Morrow, Albert Bartók, Matt Probert, Gábor Csányi*, Volker Deringer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Machine learning (ML) methods are of rapidly growing interest for materials modeling, and yet, the use of ML interatomic potentials for new systems is often more demanding than that of established density-functional theory (DFT) packages. Here, we describe computational methodology to combine the CASTEP first-principles simulation software with the on-the-fly fitting and evaluation of ML interatomic potential models. Our approach is based on regular checking against DFT reference data, which provides a direct measure of the accuracy of the evolving ML model. We discuss the general framework and the specific solutions implemented, and we present an example application to high-temperature molecular-dynamics simulations of carbon nanostructures. The code is freely available for academic research.
Original languageEnglish
Article number044803
Number of pages12
JournalJournal of Chemical Physics
Publication statusPublished - 27 Jul 2023

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