87 research outputs found
Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities
High-entropy alloys (HEAs) have attracted extensive interest due to their
exceptional mechanical properties and the vast compositional space for new
HEAs. However, understanding their novel physical mechanisms and then using
these mechanisms to design new HEAs are confronted with their high-dimensional
chemical complexity, which presents unique challenges to (i) the theoretical
modeling that needs accurate atomic interactions for atomistic simulations and
(ii) constructing reliable macro-scale models for high-throughput screening of
vast amounts of candidate alloys. Machine learning (ML) sheds light on these
problems with its capability to represent extremely complex relations. This
review highlights the success and promising future of utilizing ML to overcome
these challenges. We first introduce the basics of ML algorithms and
application scenarios. We then summarize the state-of-the-art ML models
describing atomic interactions and atomistic simulations of thermodynamic and
mechanical properties. Special attention is paid to phase predictions,
planar-defect calculations, and plastic deformation simulations. Next, we
review ML models for macro-scale properties, such as lattice structures, phase
formations, and mechanical properties. Examples of machine-learned
phase-formation rules and order parameters are used to illustrate the workflow.
Finally, we discuss the remaining challenges and present an outlook of research
directions, including uncertainty quantification and ML-guided inverse
materials design.Comment: This review paper has been accepted by Progress in Materials Scienc
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