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

Kuner, Matthew C.; Rothchild, Eric Wilson; Asta, Mark D.; Chrzan, Daryl C.

doi:10.1016/j.commatsci.2024.112924

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

Journal Article · Mon Mar 04 23:00:00 EST 2024 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2024.112924· OSTI ID:2320337

^[1]; Rothchild, Eric Wilson ^[2]; ^[1]; Chrzan, Daryl C. ^[1]

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Materials Sciences Division; University of California, Berkeley, CA (United States)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)

Grant/Contract Number:: NA0003525; AC02-05CH11231

OSTI ID:: 2320337

Alternate ID(s):: OSTI ID: 2319061

Report Number(s):: SAND--2024-02667J

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: April 2024 Vol. 238; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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

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