Multidimensional Neural Network Interatomic Potentials for CO on NaCl(100)

Sinha, Shreya; Mladineo, Bruno; Lončarić, Ivor; Saalfrank, Peter (2024) Multidimensional Neural Network Interatomic Potentials for CO on NaCl(100). The Journal of Physical Chemistry C, 128 (49). pp. 21117-21131. ISSN 1932-7447

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Abstract

The advent of machine learning (ML) models has unlocked new possibilities in the realm of interatomic potentials. We use an equivariant graph Neural Network (NN) to construct interatomic potentials for a versatile system, CO on a NaCl(100) surface, and mediate efficient large-scale atomistic simulations with ab initio molecular dynamics accuracy. We report two NN potentials, one trained on equilibrium configurations at finite temperatures (T = 30, 300 K), and the other additionally trained upon nonequilibrium trajectories of pre-excited CO adsorbates. We demonstrate first applications of the ML potentials for (i) adsorption energies and barriers for reactions, (ii) potential energy landscapes for submonolayer and monolayer coverages, (iii) vibrational spectra at finite temperatures, and (iv) vibrational relaxation dynamics. Further possible applications are discussed.

Item Type:	Article
Uncontrolled Keywords:	Ab initio molecular dynamics; Adsorption; Energy; Molecules; Potential energy
Subjects:	NATURAL SCIENCES > Physics > Condensed Matter Physics
Divisions:	Theoretical Physics Division
Projects:	Project title Project leader Project code Project type Povećanje prostorne i vremenske skale modeliranja materijala iz prvih principa pomoću strojnog učenja-ExtMatModelML Ivor Lončarić UIP-2020-02-5675 HRZZ
Depositing User:	Ana Zečević
Date Deposited:	10 Dec 2025 15:40
URI:	http://fulir.irb.hr/id/eprint/10367
DOI:	10.1021/acs.jpcc.4c05765

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