ELPH-ML: Electron-Phonon Interactions and Wannier-Based Hamiltonians for Functional Materials with machine learning
Description of the granted funding
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.
Show moreStarting year
2026
End year
2030
Granted funding
Funder
Research Council of Finland
Funding instrument
Academy research fellows
Decision maker
Scientific Council for Natural Sciences and Engineering
09.06.2026
09.06.2026
Other information
Funding decision number
376677
Fields of science
Physical sciences
Research fields
Tiiviin aineen fysiikka
Identified topics
machines, power engines