Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials

TL;DR

This paper demonstrates that passively trained machine-learning interatomic potentials can accurately and efficiently predict the thermal expansion of complex 2D carbon nanomaterials, bridging the gap between computational cost and predictive accuracy.

Contribution

The study introduces a passively trained MLIP approach that accurately reproduces phonon dispersion and thermal expansion in 2D nanomaterials, offering a computationally efficient alternative to AIMD.

Findings

MLIPs accurately reproduce phonon dispersion relations.

Passively trained MLIPs enable thermal expansion studies over wide temperature ranges.

Method is versatile for complex and novel nanomaterials.

Abstract

Examination of thermal expansion of two-dimensional (2D) nanomaterials is a challenging theoretical task with either ab-initio or classical molecular dynamics simulations. In this regard, while ab-initio molecular dynamics (AIMD) simulations offer extremely accurate predictions, but they are excessively demanding from computational point of view. On the other side, classical molecular dynamics simulations can be conducted with affordable computational costs, but without predictive accuracy needed to study novel materials and compositions. Herein, we explore the thermal expansion of several carbon-based nanosheets on the basis of machine-learning interatomic potentials (MLIPs). We show that passively trained MLIPs over inexpensive AIMD trajectories enable the examination of thermal expansion of complex nanomembranes over wide range of temperatures. Passively fitted MLIPs could also with outstanding accuracy reproduce the phonon dispersion relations predicted by density functional theory calculations. Our results highlight that the devised methodology on the basis of passively trained MLIPs is computationally efficient and versatile to accurately examine the thermal expansion of complex and novel materials and compositions using the molecular dynamics simulations.