A method for the automatic generation of a minimal basis set of structural templates for material phase-space exploration

TL;DR

This paper introduces a novel data-driven method that automatically constructs a minimal set of structural templates to efficiently predict binary phase diagrams, significantly reducing computational costs while maintaining accuracy.

Contribution

The paper presents a new approach combining data-driven techniques with ab-initio predictions to generate minimal template sets for materials space exploration, enabling faster phase diagram predictions.

Findings

Predicts binary convex hulls with accuracy comparable to extensive EA searches.

Reduces computational time by a factor of 25.

Extensible to ternary and multinary systems for high-throughput screening.

Abstract

We present a novel method for predicting binary phase diagrams through the automatic construction of a minimal basis set of representative templates. The core assumption is that any materials space can be divided into a small number of regions with similar chemical tendencies and bonding properties, and that a minimal set of templates can efficiently represent the key chemical trends across the different regions. By combining data-driven techniques with ab-initio crystal structure prediction, we can efficiently partition the materials space and construct templates reflecting variations in chemical behavior. Preliminary results demonstrate that our method predicts binary convex hulls with accuracy comparable to resource-intensive EA searches, while achieving a significant reduction in computational time (by a factor of 25). The method can be extended to ternary and multinary systems, enabling efficient high-throughput exploration and mapping of complex material spaces. By providing a transformative solution for high-throughput materials discovery, our approach paves the way for uncovering advanced quantum materials and accelerating in silico design.