Computational alchemy clarifies origins of alloy strengthening

Liang, Aoyan; Bertin, Nicolas; Zhou, Xinran; Aubry, Sylvie; Bulatov, Vasily V.

doi:10.1038/s41524-025-01910-0

Title: Computational alchemy clarifies origins of alloy strengthening

Journal Article · Thu Dec 18 00:00:00 UTC 2025 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-025-01910-0 · OSTI ID:3017838

Liang, Aoyan ^[1]; Bertin, Nicolas ^[2]; Zhou, Xinran ^[3]; Aubry, Sylvie ^[2]; Bulatov, Vasily V. ^[2]

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Univ. of California, Los Angeles, CA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Solid solution strengthening (SSS) is widely used to enhance mechanical properties of metals. Originally developed for dilute alloys, classical SSS theories are presently challenged by the rise of complex concentrated alloys (CCA) with nearly equiatomic compositions. Here, we propose and develop a method of “computational alchemy” in which interatomic interactions are modified to systematically vary two key physical parameters defining SSS - atomic size misfit and elastic stiffness misfit - over a maximally wide range of two misfits. The resulting alchemical alloys are subjected to massive (~10⁸ atoms) molecular dynamics (MD) simulations reproducing full complexity of plastic strength response. At variance with prevailing views, stiffness misfit is observed to contribute to SSS on par if not more than size misfit. Furthermore, depending on exactly how two misfits are combined, they result in synergistic (amplification) or antagonistic (compensation) effect on alloy strengthening. Unlike real CCAs in which each component element comes with its own specific size and stiffness, our alchemical model alloys span the space of two misfits continuously revealing trends in alloy strengthening unrecognized so far. Our study demonstrates unique value of intentionally unrealistic models for gaining deep physical insights into material behaviors that are difficult to reveal otherwise.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-05CH11231; AC52-07NA27344

OSTI ID:: 3017838

Report Number(s):: LLNL--JRNL-2016302; ark:/13030/qt5433q92c; https://escholarship.org/uc/item/5433q92c

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 12; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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