首页|New Data from University of Seville Illuminate Findings in Machine Learning (Cha llenges Reconciling Theory and Experiments In the Prediction of Lattice Thermal Conductivity: the Case of Cu-based Sulvanites)
New Data from University of Seville Illuminate Findings in Machine Learning (Cha llenges Reconciling Theory and Experiments In the Prediction of Lattice Thermal Conductivity: the Case of Cu-based Sulvanites)
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Investigators publish new report on Ma chine Learning. According to news reporting out of Seville, Spain, by NewsRx edi tors, research stated, "The exploration of large chemical spaces in search of ne w thermoelectric materials requires the integration of experiments, theory, simu lations, and data science. The development of high-throughput strategies that co mbine DFT calculations with machine learning has emerged as a powerful approach to discovering new materials." Financial supporters for this research include European Union Next Generation EU /PRTR, Comunidad de Madrid, MICIU/AEI, Red Espanola de Supercomputacion, RES. Our news journalists obtained a quote from the research from the University of S eville, "However, experimental validation is crucial to confirm the accuracy of these workflows. This validation becomes especially important in understanding t he transport properties that govern the thermoelectric performance of materials since they are highly influenced by synthetic, processing, and operating conditi ons. In this work, we explore the thermal conductivity of Cu-based sulvanites by using a combination of theoretical and experimental methods. Previous discrepan cies and significant variations in reported data for Cu3VS4 and Cu3VSe4 are expl ained using the Boltzmann Transport Equation for phonons and by synthesizing wel l-characterized defect-free samples."
SevilleSpainEuropeCyborgsEmergin g TechnologiesMachine LearningUniversity of Seville
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