Transferable machine learning interatomic potential for carbon hydrogen systems

Abstract

In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon–hydrogen (C–H) systems. The ANN potential was trained on a dataset of C–H clusters obtained through density functional theory (DFT) calculations. Through comprehensive evaluations against DFT results, including predictions of geometries and formation energies across 0D–3D systems comprising C and C–H, as well as modeling various chemical processes, the ANN potential demonstrated exceptional accuracy and transferability. Its capability to accurately predict lattice dynamics, crucial for stability assessment in crystal structure prediction, was also verified through phonon dispersion analysis. Notably, its accuracy and computational efficiency in calculating force constants facilitated the exploration of complex energy landscapes, leading to the discovery of a novel C polymorph. These results underscore the robustness and versatility of the ANN potential, highlighting its efficacy in advancing computational materials science by conducting precise atomistic simulations on a wide range of C–H materials.