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

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.

Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons.

Machine learning (ML) interatomic potentials (ML-IAPs) are generated for alkane and polyene hydrocarbons using on-the-fly adaptive sampling and a sparse Gaussian process regression (SGPR) algorithm. The ML model is generated based on the PBE+D3 level of density functional theory (DFT) with molecular dynamics (MD) for small alkane and polyene molecules. Intermolecular interactions are also trained with clusters and condensed phases of small molecules.

Two Liquid-Liquid Phase Transitions in Confined Water Nanofilms.

The stories behind supercooled bulk and confined water can be different. Bulk water has a metastable liquid-liquid phase transition at deeply supercooled conditions, but the existence of such a phenomenon in confined water is in question. Herein we show simulation results of first-order phase transitions between high- and low-density liquid (HDL and LDL) in confined water in both positive and negative pressures.