Work

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

Year: 2025

Type: article

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.

Source: The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c04912

Authors: Colin Bousige, Anouar-Akacha Delenda, Abdul-Rahman Allouche, Pierre Mignon

Institutions: Lyon 1 Université, Centre National de la Recherche Scientifique, Laboratoire des Multimatériaux et Interfaces, Institut Lumière Matière

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: Agence Nationale de la Recherche, Grand Équipement National De Calcul Intensif, Institut National de Physique Nucléaire et de Physique des Particules

Awards: Borophene Chemical Synthesis, G7997823257