Work

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

Year: 2018

Type: article

Source: Computational Materials Science

DOI: 10.1016/j.commatsci.2018.09.031

Authors: Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L.W. Hart, Alexander V. Shapeev

Institutions: Skolkovo Institute of Science and Technology, Brigham Young University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: bronze

Funders: U.S. Department of Energy, Center for Integrated Nanotechnologies, Russian Science Foundation, Office of Science, Multidisciplinary University Research Initiative, Office of Naval Research, Sandia National Laboratories, Los Alamos National Laboratory

Awards: G11630931, G1883336974, G280414146, G286898171, G2872252334, G288067973, G3700687868, G413247060, G4191641197, G4509834540, G4673772109, G5657628048, G5964803810, G680142904, G69704028, G75892134, G8406156227, G872931124, G8876996369

OpenAlex ID: W2809779474

FWCI: 9.358

Cites: 54

Cited by: 362

Related to: 10