Predicting The Synthesizability and Properties of Disordered Materials from First-Principles

The successful development and manufacturing of new materials, for applications ranging from wear resistant coatings for cutting tools and thermal protection barriers in aerospace engineering to new catalysts and photovoltaics, as well as materials for batteries and electronics, depends on computational thermodynamics to predict synthesizability and stability. Thermodynamic models for synthesizability must incorporate entropy, which is particularly important at high temperature for multi-element materials [1, 2]. Descriptors and thermodynamic models have been developed based on the thermodynamic density of states extracted from ensembles of ordered calculations in the AFLOW repository [3, 4] to predict the synthesizability of new disordered materials such as high entropy carbides [5, 6]. Similar methods are now being combined with machine-learning to investigate high-entropy rare-earth silicates for thermal and environmental barriers in gas turbines [7].<br/><br/>[1] Toher et al., npj Comput. Mater. 5, 69 (2019).<br/>[2] Brahlek et al., APL Mater. 10, 110902 (2022).<br/>[3] Oses et al., Comput. Mater. Sci. 217, 111889 (2023).<br/>[4] Esters et al., Comput. Mater. Sci. 216, 111808 (2023).<br/>[5] Sarker, Harrington et al., Nature Commun. 9, 4980 (2018).<br/>[6] Oses, Toher, and Curtarolo, Nature Rev. Mater. 5, 295-309 (2020).<br/>[7] Toher et al., Materialia 28, 101729 (2023).