CUED Publications database

Machine-learned multi-system surrogate models for materials prediction

Nyshadham, C and Rupp, M and Bekker, B and Shapeev, AV and Mueller, T and Rosenbrock, CW and Csányi, G and Wingate, DW and Hart, GLW (2019) Machine-learned multi-system surrogate models for materials prediction. npj Computational Materials, 5.

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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, NbNi) with 10 different species and all possible fcc, bcc and hcp structures up to 8 atoms in the unit cell, 15 950 structures in total. We find that the deviation of prediction errors when increasing the number of simultaneously modeled alloys is less than 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.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 02 May 2019 01:15
Last Modified: 18 Feb 2021 15:50
DOI: 10.1038/s41524-019-0189-9