ArticleJournal of chemical information and modeling2025
Machine Learning Methodologies Applied to Magnetocaloric Perovskites Discovery.
Article in Journal of chemical information and modeling, 2025. 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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6 authors.
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Abstract
Traditionally, designing novel materials involves exploring new compositions guided by insights from previous work, relying on a trial-and-error approach, where continuous synthesis and characterization proceed until the properties meet the improvements. This method is inefficient due to the challenges of exploring vast chemical spaces. In this study, a machine-learning-based methodology is developed to assist the design from available data in the literature, allowing us to test in silico more than 1.2 million compositions. Two databases with 1227 inputs were created from published studies. Four machine learning (ML) models were trained over the feature sets using 517 compositional features (generated from 58 atomic properties) to predict magnetocaloric properties of perovskites: Curie temperature (
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