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

TL;DR

This paper introduces a coevolutionary computational method to predict optimal materials with desired properties from the entire chemical space, demonstrating its effectiveness with findings on diamond's hardness and bcc-Fe's magnetization.

Contribution

It presents a novel coevolutionary approach combined with chemical space restructuring and Pareto optimization for materials discovery.

Findings

Diamond is the hardest possible material.

bcc-Fe has the highest zero-temperature magnetization.

Method effectively predicts materials with optimal properties.

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.