Transferable machine learning interatomic potential for carbon hydrogen systems.

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.

Surface reconstructions and premelting of the (100) CaF surface.

In this work, surface reconstructions on the (100) surface of CaF2 are comprehensively investigated. The configurations were explored by employing the Minima Hopping Method (MHM) coupled to a machine-learning interatomic potential, that is based on a charge equilibration scheme steered by a neural network (CENT). The combination of these powerful methods revealed about 80 different morphologies for the (100) surface with very similar surface formation energies differing by not more than 0.

A fingerprint based metric for measuring similarities of crystalline structures.

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric.