Ab initio property predictions of quinary solid solutions using small binary cells

• Published in: Computational materials science Vol. 238; no. April 2024
• Main Authors: Kuner, Matthew C., Rothchild, Eric Wilson, Asta, Mark D., Chrzan, Daryl C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier 05.03.2024
• Subjects: disorder; HEAs; high entropy alloys; high-throughput density functional theory; HT-DFT; MATERIALS SCIENCE; random solid solutions; set of small ordered structures; small structures; SSOS
• ISSN: 0927-0256
• DOI: 10.1016/j.commatsci.2024.112924

The Set of Small Ordered Structures (SSOS) approach is an ab initio technique for modelling random solid solutions in which many small structures are averaged so that their correlation functions match those of a desired composition. SSOS has been shown to be effective in reducing the cost of density functional theory calculations relative to other well-known techniques such as cluster expansions and special quasirandom structures for modelling solid solutions. Here in this work, we demonstrate that SSOS’s can be constructed using cells with only a subset of elements while still accurately modelling multi-component systems. Specifically, we show that small binary cells can effectively model two quinary high entropy alloys – NbTaTiHfZr and MoNbTaVW – accurately capturing properties such as formation energy, lattice parameters, elastic constants, and root-mean-square atomic displacements. Overall, this insight is useful for those looking to construct databases of such small structures for predicting the properties of multi-component solid solutions, as it greatly decreases the number of structures that needs to be considered.