Electronic manifolds for extrapolative alloy discovery

Abstract

This study presents a computationally efficient framework for accelerated alloy discovery that uses the non-interacting electron density to capture intrinsic structure-property relationships in refractory high-entropy alloys (HEAs). Unlike state-of-the-art approaches relying on expensive, self-consistent density functional theory calculations, our method employs the non-interacting electron density as the primary structural descriptor. By extracting physical features through directionally resolved two-point spatial correlations and compressing them via Principal Component Analysis, we efficiently map the design space. Coupling these descriptors with Bayesian active learning, we achieve a normalized mean absolute error (NMAE) of <2% for the bulk modulus of Al-Nb-Ti-Zr alloys using only 10 training samples (<0.2% of the dataset). Furthermore, we demonstrate that the model learns an electronic packing manifold that is transferable within the refractory BCC alloy family. Validated on a distinct 7-component refractory system (Mo-Nb-Ta-Ti-V-W-Zr) containing four elements entirely absent from the training data, the framework enables zero-shot transfer within the refractory BCC alloy class. Moreover, by augmenting the base model with just 20 samples from the target domain, we achieve high-fidelity predictions (NMAE<3%) for 7-component alloys, reducing data acquisition costs by orders of magnitude compared to standard workflows. A controlled comparison confirms that composition-based descriptors under the identical pipeline do not reach the same accuracy threshold within the same sample budget, establishing that the spatial autocorrelation encoding of the non-interacting electron density provides information beyond elemental composition statistics alone.