In this work, we present a highly accurate spectral neighbor analysis
potential (SNAP) model for molybdenum (Mo) developed through the rigorous
application of machine learning techniques on large materials data sets.
Despite Mo's importance as a structural metal, existing force fields for Mo
based on the embedded atom and modified embedded atom methods still do not
provide satisfactory accuracy on many properties. We will show that by fitting
to the energies, forces and stress tensors of a large density functional theory
(DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can
be developed that achieves close to DFT accuracy in the prediction of a broad
range of properties, including energies, forces, stresses, elastic constants,
melting point, phonon spectra, surface energies, grain boundary energies, etc.
We will outline a systematic model development process, which includes a
rigorous approach to structural selection based on principal component
analysis, as well as a differential evolution algorithm for optimizing the
hyperparameters in the model fitting so that both the model error and the
property prediction error can be simultaneously lowered. We expect that this
newly developed Mo SNAP model will find broad applications in large-scale,
long-time scale simulations.