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However, their natural scarcity, combined with the vast compositional space in multicomponent systems, renders traditional trial\u2010and\u2010error approaches both time\u2010consuming and cost\u2010prohibitive, posing an emergent challenge for modern high\u2010tech applications. Here, we establish a task\u2010specific active\u2010learning framework with empirical fine\u2010tuning to accelerate ZTE materials' design. By mining sparse experimental datasets, we identified a high\u2010potential compositional region and uncovered several novel low\u2010expansion alloys. Among them, Cr\n                    <jats:sub>1.4<\/jats:sub>\n                    Co\n                    <jats:sub>8.9<\/jats:sub>\n                    Ni\n                    <jats:sub>30.5<\/jats:sub>\n                    Fe\n                    <jats:sub>59<\/jats:sub>\n                    exhibits a low coefficient of thermal expansion of , and a high Curie temperature reaching 580 . Real\u2010time in situ neutron and synchrotron x\u2010ray diffraction confirm its single\u2010phase face\u2010centered 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\u2010expansion performance, providing data\u2010driven insights into the magnetovolume origins of this behavior. This work not only yields a high\u2010performance dimensionally stable alloy, but also demonstrates how integrating physical insights with data\u2010driven design can accelerate advanced materials\u00a0development.\n                  <\/jats:p>","DOI":"10.1002\/adma.74942","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:57:29Z","timestamp":1789102649000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Discovery of Multicomponent Invar Materials With Low Thermal Expansion, High Curie Temperature, and Superior Phase Stability Assisted by Active Learning"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9054-7856","authenticated-orcid":false,"given":"Song","family":"Yang","sequence":"first","affiliation":[{"name":"Institute of Solid State Chemistry School of Chemistry and Biological Engineering University of Science and Technology Beijing  Beijing 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