Interpretable Machine Learning Model on Thermal Conductivity Using Publicly Available Datasets and Our Internal Lab Dataset
NK Barua, E Hall, Y Cheng, AO Oliynyk… - Chemistry of …, 2024 - ACS Publications
Machine learning (ML), a subdiscipline of artificial intelligence studies, has gained
importance in predicting or suggesting efficient thermoelectric materials. Previous ML …
importance in predicting or suggesting efficient thermoelectric materials. Previous ML …
Crystal growth of intermetallic thermoelectric materials
Considering that more than half of all primary energy, ie, fossil fuels that mankind consumes
is lost in the form of waste heat, the solid-state conversion of heat to electricity that …
is lost in the form of waste heat, the solid-state conversion of heat to electricity that …
Hyperfine interactions in dilute Se doped Fe x Sb 1 − x bulk alloy
Abstract Hyperfine Interaction technique like Mossbauer spectroscopy is a very sensitive tool
to study the local probe interactions in Iron doped alloys and compounds. We report here the …
to study the local probe interactions in Iron doped alloys and compounds. We report here the …
Hyperfine interaction study of Te doped FexSb1-x alloy
Hyperfine Interaction technique like Mossbauer spectroscopy is a very sensitive tool to study
the local probe interactions in Iron rich alloys. We report here the Mossbauer study of the …
the local probe interactions in Iron rich alloys. We report here the Mossbauer study of the …