Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential

Abstract

We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these elements. This ML interatomic potential is trained using the Gaussian approximation potential (GAP) framework and an extensive dataset of C-H configurations obtained from density functional theory. The dataset is constructed through iterative training and structure-search techniques that generate a broad range of configurations to comprehensively sample the potential energy surface. Furthermore, the dataset is supplemented with relevant bulk, molecular, and high-pressure structures. Finally, long-range van der Waals interactions are added as a locally parametrized model. The accuracy and generality of the potential are validated through the analysis of different simulations under a wide range of conditions, including weak interactions, high temperature, and high pressure. We show that our CH GAP model describes different problems such as the formation of simple and complex alkanes, aromatic hydrocarbons, hydrogenated amorphous carbon (a-C:H), and CH systems at extreme conditions, while retaining good accuracy for pure carbon materials. We use this model to generate hydrocarbons of different sizes and complexity without prior knowledge of organic chemistry rules, and to highlight intrinsic limitations to the simultaneous description on intra and intermolecular interactions within a single computational framework. Our general-purpose ML interatomic potential has the capability to significantly advance research in the field of H-containing carbon materials and compounds, particularly in the areas where longer dynamics, reactivity and large-scale effects may be important.