Modeling high-entropy transition-metal alloys with alchemical compression
Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer, and Sandip De, Michele Ceriotti

TL;DR
This paper introduces a compressed chemical representation for modeling high-entropy transition-metal alloys, enabling efficient and accurate simulations of complex multi-element systems and guiding alloy design.
Contribution
The authors develop a novel alchemical compression scheme that reduces the complexity of modeling 25 transition metals, allowing scalable and accurate potential construction for high-entropy alloys.
Findings
The model achieves semi-quantitative accuracy for prototypical alloys.
It remains stable when extrapolating beyond training data.
The framework reveals element segregation patterns and guides alloy design.
Abstract
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys, and is remarkably stable when extrapolating to structures outside…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Materials Characterization Techniques · High Entropy Alloys Studies
