Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

Yin, Junqi; Pei, Zongrui; Gao, Michael C.

doi:10.1038/s43588-021-00139-3

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

Journal Article · Mon Oct 18 00:00:00 EDT 2021 · Nature Computational Science

DOI:https://doi.org/10.1038/s43588-021-00139-3· OSTI ID:1827009

^[1]; ^[2]; Gao, Michael C. ^[3]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); National Energy Technology Lab. (NETL), Albany, OR (United States)
National Energy Technology Lab. (NETL), Albany, OR (United States)

Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order–disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. Here, we propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order–disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.

View Accepted Manuscript (DOE)

Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1827009

Alternate ID(s):: OSTI ID: 1846867

Journal Information:: Nature Computational Science, Journal Name: Nature Computational Science Journal Issue: 10 Vol. 1; ISSN 2662-8457

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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