ELPH-ML: Electron-Phonon Interactions and Wannier-Based Hamiltonians for Functional Materials with machine learning
Bidragets beskrivning
This project develops artificial-intelligence (AI)-based tools to design new energy-harvesting materials and highly sensitive gas sensors. Finland's effort to reach carbon neutrality by 2035 requires technologies that can turn wasted heat into electricity and detect harmful gases with high accuracy. The research uses computer simulations and machine-learning models to predict how materials behave at the atomic level. The work will be carried out at Aalto University in close collaboration with internal partners. My previous work in this area gives me a strong foundation to carry out the project effectively. The results will support safer environments, cleaner energy systems, and contribute to the UN Sustainable Development Goals.
Visa merStartår
2026
Slutår
2030
Beviljade finansiering
Finansiär
Finlands Akademi
Typ av finansiering
Akademiforskare
Beslutfattare
Forskningsrådet för naturvetenskap och teknik
09.06.2026
09.06.2026
Övriga uppgifter
Finansieringsbeslutets nummer
376677
Vetenskapsområden
Fysik
Forskningsområden
Tiiviin aineen fysiikka
Identifierade teman
machines, power engines