Accurate Machine-learned Potential for Molybdenum

Chi’s paper on “Accurate force field for molybdenum by machine learning large materials data” has just been published in Physical Review Materials. This work addresses a crucial gap in the available force field for Mo. We will show that by fitting to the energies, forces, and stress tensors of a large DFT dataset on a diverse set of Mo structures, a Mo Spectral Neighbor Analysis Potential (SNAP) model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc.

Examples and parameters of the new potential can be obtained at our Github page.