A Portable Data Set for Borophene Growth Modeling with Reactive Neural Network Potentials

Colin Bousige1ORCID iD, Anouar-Akacha Delenda2ORCID iD, Abdul-Rahman Allouche2ORCID iD, and Pierre Mignon2ORCID iD

1 Universite Claude Bernard Lyon 1, CNRS, LMI UMR 5615, Villeurbanne F-69100, France.

2 Universite Claude Bernard Lyon 1, CNRS, iLM UMR 5306, Villeurbanne F-69100, France.

The Journal of Physical Chemistry C 2025 129 (41), 18760-18771

DOI

Abstract

In this study, we develop and validate machine learned interaction potentials (MLIPs) for reactive simulation of borophene on metal substrates. A versatile training data set is constructed to accurately represent both extended and reactive borophene structures. It should be portable to train any MLIP architecture. Indeed, three generations of MLIPs, namely n2p2, DeePMD and NNMP, are trained and validated against density functional theory (DFT) calculations. Our results demonstrate the capability of the MLIPs to accurately reproduce DFT-calculated structures, energies, and forces. We finally show that it is possible to use these MLIPs to simulate the growth of borophene on a silver substrate.