Discovery of Multicomponent Invar Materials With Low Thermal Expansion, High Curie Temperature, and Superior Phase Stability Assisted by Active Learning.

Abstract

Zero thermal expansion (ZTE) materials provide exceptional dimensional stability under temperature fluctuations, making them indispensable for high-precision instrumentation and extreme environments. However, their natural scarcity, combined with the vast compositional space in multicomponent systems, renders traditional trial-and-error approaches both time-consuming and cost-prohibitive, posing an emergent challenge for modern high-tech applications. Here, we establish a task-specific active-learning framework with empirical fine-tuning to accelerate ZTE materials' design. By mining sparse experimental datasets, we identified a high-potential compositional region and uncovered several novel low-expansion alloys. Among them, Cr_1.4Co_8.9Ni_30.5Fe₅₉ exhibits a low coefficient of thermal expansion of <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>1.9</mn> <mo>±</mo> <mn>0.3</mn> <mo>×</mo> <msup><mn>10</mn> <mrow><mo>-</mo> <mn>6</mn></mrow> </msup> <mspace></mspace> <msup><mi>K</mi> <mrow><mo>-</mo> <mn>1</mn></mrow> </msup> </mrow> </math> , and a high Curie temperature reaching 580 <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>K</mi></math> . Real-time in situ neutron and synchrotron x-ray diffraction confirm its single-phase face-centered cubic structure, excellent ductility, and robust phase stability against thermal and mechanical stimuli. Crucially, machine learning analysis pinpointed six key descriptors highly correlated with low-expansion performance, providing data-driven insights into the magnetovolume origins of this behavior. This work not only yields a high-performance dimensionally stable alloy, but also demonstrates how integrating physical insights with data-driven design can accelerate advanced materials development.