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An analytical method to quantify the statistics of energy landscapes in random solid solutions

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journal contribution
posted on 2022-09-26, 19:59 authored by Ritesh Jagatramka, Chu Wang, Matthew DalyMatthew Daly
Recent studies of concentrated solid solutions have highlighted the role of varied solute interactions in the determination of a wide variety of mesoscale properties. These solute interactions emerge as spatial fluctuations in potential energy, which arise from local variations in the chemical environment. Although observations of potential energy fluctuations are well documented in the literature, there remains a paucity of methods to determine their statistics. Here, we present a set of analytical equations to quantify the statistics of potential energy landscapes in randomly arranged solid solutions. Our approach is based on a reparameterization of the relations of the embedded-atom method in terms of the solute coordination environment. The final equations are general and can be applied to different crystal lattices and energy landscapes, provided the systems of interest can be described by sets of coordination relations. We leverage these statistical relations to study the cohesive energy and generalized planar fault energy landscapes of several different solid solutions. Analytical predictions are validated using molecular statics simulations, which find excellent agreement in most cases. The outcomes of this analysis provide new insights into phase stability and the interpretation of ‘local’ planar fault energies in solid solutions, which are topics of ongoing discussion within the community.

Funding

CAREER: Order-induced heterogeneities in the deformation behavior of FCC concentrated solid solutions | Funder: Directorate for Mathematical & Physical Sciences | Grant ID: 2144451

History

Citation

Jagatramka, R., Wang, C.Daly, M. (2022). An analytical method to quantify the statistics of energy landscapes in random solid solutions. Computational Materials Science, 214, 111763-. https://doi.org/10.1016/j.commatsci.2022.111763

Publisher

Elsevier BV

Language

  • en

issn

0927-0256

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