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Active Learning for Thermoelectric Power Factor by Rayhana Karar is a document available to read on EtoBox.
This article discusses the integration of machine learning and first-principles calculations to predict the power factors of diamond-like thermoelectric materials. An active learning loop is established, which iteratively refines predictions by selecting candidates based on a high-throughput theoretical database and verifying their properties through computational methods. The study identifies binary pnictides and certain chalcogenides as promising candidates for high power factors, demonstrating the effect
- Author
- Rayhana Karar
- Language
- EN