Machine-learned multi-system surrogate models for materials prediction

Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker, Alexander V. Shapeev, Tim Mueller, Conrad W. Rosenbrock, Gábor Csányi, David W. Wingate, Gus L.W. Hart

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)

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, and NbNi) with 10 different species and all possible fcc, bcc, and hcp structures up to eight 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 <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.

Original languageEnglish
Article number51
Journalnpj Computational Materials
Volume5
Issue number1
DOIs
Publication statusPublished - 1 Dec 2019

Fingerprint

Dive into the research topics of 'Machine-learned multi-system surrogate models for materials prediction'. Together they form a unique fingerprint.

Cite this