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

Zahed Allahyari, Artem R. Oganov

Результат исследований: Вклад в журналСтатьярецензирование

15 Цитирования (Scopus)


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.

Язык оригиналаАнглийский
Номер статьи55
Журналnpj Computational Materials
Номер выпуска1
СостояниеОпубликовано - 14 мая 2020


Подробные сведения о темах исследования «Coevolutionary search for optimal materials in the space of all possible compounds». Вместе они формируют уникальный семантический отпечаток (fingerprint).