Electronic manifolds for extrapolative alloy discovery
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918467238625280 |
|---|---|
| author | Ray, Pranoy Bhowmik, Sayan Suryanarayana, Phanish Kalidindi, Surya R. Medford, Andrew J. |
| author_facet | Ray, Pranoy Bhowmik, Sayan Suryanarayana, Phanish Kalidindi, Surya R. Medford, Andrew J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06953 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Electronic manifolds for extrapolative alloy discovery Ray, Pranoy Bhowmik, Sayan Suryanarayana, Phanish Kalidindi, Surya R. Medford, Andrew J. Materials Science Data Analysis, Statistics and Probability 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. |
| title | Electronic manifolds for extrapolative alloy discovery |
| topic | Materials Science Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2603.06953 |