ArticleRSC advances2026
Experimental-data-driven thermal conductivity prediction and inverse composition design for alloys.
Article in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
This work develops a data-driven framework for predicting the thermal conductivity of metals and multi-component alloys and for inversely proposing compositions that meet a target conductivity. We collect, to our knowledge, the largest experimental dataset containing 6259 data points spanning 49 elements and temperatures from 0 to 1400 K. Using alloy composition and temperature as inputs, we train and benchmark several regression models and obtain high predictive accuracy reaching
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.