A neural network potential for high throughput screening of the energetics and thermodynamical stabilities of non-stoichiometric Chromium Sulfides

Machine learning potentials (MLPs) have recently become a powerful tool in computational materials science due to their ability to bridge the quantum-mechanical accuracy of ab initio methods to large systems with a linear scaling. We present a neural network potential (NNP) trained on data generated using density functional theory (DFT) to predict the energies and stabilities for a wide range of stoichiometries of Cr-S structures with Cr vacancies. A preliminary investigation of the stable phases is performed using the cluster expansion method (CE). Crystal structures are exhaustively enumerated for a more fine-grained stoichiometry range. The NNP shows an excellent transferability to unseen stoichiometries, which is utilized in identifying the crystal structures of the stable and metastable phases for the full stoichiometric space. The phonon bandstructures are then predicted with an excellent agreement with DFT over the full stoichiometry space. Furthermore, the dynamical stabilities of some phases are evaluated using classical molecular dynamics (MD) driven by the NNP. The employed methodology acts as a systematic approach using (MLPs) to search the structure space and investigate the phonon and dynamical stabilities for a wide range of stoichiometries of a given material.