Machine-learned Multi-system Surrogate Models for Materials Prediction
Abstract
Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the computational cost. We demonstrate surrogate models that simultaneously interpolate energies of different materials on a dataset of 10 binary alloys (AgCu, AlFe, AlMg, AlNi, AlTi, CoNi, CuFe, CuNi, FeV, andNbNi) with 10 different species and all possible fcc, bcc, and hcp structures up to eight atoms in the unit cell, 15,950 structures intotal. We find that the deviation of prediction errors when increasing the number of simultaneously modeled alloys is <1 meV/atom. Several state-of-the-art materials representations and learning algorithms were found to qualitatively agree on the prediction errors of formation enthalpy with relative errors of <2.5% for all systems.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 18, 2019
- Accession Number
- AD1097843
Entities
People
- Alexander V. Shapeev
- Brayden Bekker
- Chandramouli Nyshadham
- Conrad W. Rosenbrock
- David W. Wingate
- Gus L. Hart
- Gábor Csányi
- Matthias Rupp
- Tim Mueller
Organizations
- Brigham Young University