Machine Learning Methodologies Applied to Magnetocaloric Perovskites Discovery
Luis E. Castro-Anaya; Eduardo Marese; Jaime A. Lozano; Guilherme Fidelis Peixer; Jader R. Barbosa; Sergio Yesid Gómez González · 2025 · Journal of Chemical Information and Modeling
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
High Resolution Image Download MS PowerPoint Slide 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
Abstract by Luis E. Castro-Anaya; Eduardo Marese; Jaime A. Lozano; Guilherme Fidelis Peixer; Jader R. Barbosa; Sergio Yesid Gómez González, Journal of Chemical Information and Modeling (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1021/acs.jcim.4c01944