# Accurate Force Field for Molybdenum by Machine Learning Large Materials Data

## TL;DR

This paper introduces a machine learning-based spectral neighbor analysis potential (SNAP) model for molybdenum, achieving near-DFT accuracy across various properties, enabling advanced large-scale simulations.

## Contribution

The paper develops a highly accurate machine learning force field for molybdenum using large datasets and systematic optimization, surpassing previous models in accuracy.

## Key findings

- Achieves near-DFT accuracy for molybdenum properties
- Uses principal component analysis for structural selection
- Employs differential evolution for hyperparameter optimization

## Abstract

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.