5 papers
Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials
Shoeb Athar, Philippe Jund
The discovery of high-performance thermoelectric (TE) materials for advancing green energy harvesting from waste heat is an urgent need in the context of looming energy crisis and…
TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
Léo Grinsztajn, Klemens Flöge, Oscar Key +23
The first tabular foundation model, TabPFN, and its successor TabPFNv2 have impacted tabular AI substantially, with dozens of methods building on it and hundreds of applications ac…
Robust Machine Learning Framework for Reliable Discovery of High-Performance Half-Heusler Thermoelectrics
Shoeb Athar, Adrien Mecibah, Philippe Jund
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor exper…
Carbogels for sustainable and scalable thermoelectric applications
Shoeb Athar, Jeremy Guazzagaloppa, Fabrice Boyrie +2
Thermoelectric generators (TEGs) based on commercially used thermal super-insulating materials can facilitate sustainable and large-scale ambient waste heat recovery while bequeath…
Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials
Shoeb Athar, Adrien Mecibah, Philippe Jund
Machine Learning (ML) driven discovery of novel and efficient thermoelectric (TE) materials warrants experimental TE datasets of high volume, diversity, and quality. While the larg…