Work

Coevolutionary search for optimal materials in the space of all possible compounds

Year: 2020

Type: article

Abstract: Over the past decade, evolutionary algorithms, data mining, and other methods showed great success in solving the main problem of theoretical crystallography: finding the stable structure for a given chemical composition. Here, we develop a method that addresses the central problem of computational materials science: the prediction of material(s), among all possible combinations of all elements, that possess the best combination of target properties. This nonempirical method combines our new coevolutionary approach with the carefully restructured “Mendelevian” chemical space, energy filtering, and Pareto optimization to ensure that the predicted materials have optimal properties and a high chance to be synthesizable. The first calculations, presented here, illustrate the power of this approach. In particular, we find that diamond (and its polytypes, including lonsdaleite) are the hardest possible materials and that bcc-Fe has the highest zero-temperature magnetization among all possible compounds.

Source: npj Computational Materials

DOI: 10.1038/s41524-020-0322-9

Authors: Zahed Allahyari, Artem R. Oganov

Institutions: Skolkovo Institute of Science and Technology, Moscow Institute of Physics and Technology, Northwestern Polytechnical University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,680

Funders: Russian Science Foundation, Moscow Institute of Physics and Technology