Sparse Gaussian Process Regression-Based Machine Learned First-Principles Force-Fields for Saturated, Olefinic, and Aromatic Hydrocarbons

Abstract

Universal machine learning (ML) interatomic potentials (IAPs) for saturated, olefinic, and aromatic hydrocarbons are generated by using the Sparse Gaussian process regression algorithm. The universal potentials are obtained by combining the potentials for the previously trained alkane/polyene systems and the potentials generated with the presently trained cyclic/aromatic hydrocarbon systems, along with the newly trained cross-terms between the two systems. The ML-IAPs have been trained using the PBE + D3 level of density functional theory for the on-the-fly adaptive sampling of various hydrocarbon molecules and these clusters composed of small molecules. We tested the ML-IAPs and found that they correctly predicted the structures and energies of the β-carotene monomer and dimer. Also, the simulations of liquid ethylene reproduced the molecular volume and the simulations of toluene crystals reproduced higher stability of the α-phase over the β-phase. These ab initio-level force-fields could eventually evolve toward universal organic/polymeric/biomolecular systems.