Year: 2026
Type: article
Abstract: The vast compositional space of high-entropy alloys (HEA) holds promise for next-generation functional materials, yet its exploration is stifled by a combinatorial bottleneck: conventional first-principles accuracy is computationally prohibitive for complex disordered lattices, while black-box machine learning (ML) lacks physical interpretability. We overcome this by establishing a minimal-supercell principle, demonstrating that the magnetostructural behaviour is an emergent feature of local atomic environments rather than long-range configurations. This enables a physics-informed ML framework that inverts the conventional screening approach, transitioning from limited local identification to global design-space mapping, reducing computational time by 40 - 60% without compromising accuracy. Applying this framework to MM'X family uncovers a fundamental design dichotomy: mean electronic descriptors govern baseline phase stability, while dispersion descriptors, specifically the magnetic-moment dispersion, drive the critical transformation response. This analysis reveals an intrinsic stability-performance trade-off, where the disorder required to maximise the transformation driving force inevitably penalises thermodynamic stability. By quantifying this Pareto frontier, we propose a hierarchical tuning strategy that decouples phase transition temperature from hysteresis, providing a scalable paradigm to transform HEA exploration from serendipitous discovery into rational design.
Source: Nature Communications
Institution: Universidad de Sevilla
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Awards: G1011186717, G1092424111, G1175115578, G1561925909, G1624672878, G1855990336, G1866988902, G2065685387, G2148124073, G2262748287, G2309174360, G252646220, G2550080368, G2601397997, G294895050, G300979063, G3252294556, G3480869486, G3518538256, G3681454997, G4042783231, G414485594, G425668148, G430777504, G464105488, G5034004949, G5080475149, G5421974903, G6532093564, G7193178855, G7434849621, G745419941, G8347645112, G8616871631, G99296377
OpenAlex ID: W7204158006
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