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.
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Starting year

2026

End year

2030

Granted funding

Ransell DSouza Orcid -palvelun logo
699 800 €

Funder

Research Council of Finland

Funding instrument

Academy research fellows

Decision maker

Scientific Council for Natural Sciences and Engineering
09.06.2026

Other information

Funding decision number

376677

Fields of science

Physical sciences

Research fields

Tiiviin aineen fysiikka

Identified topics

machines, power engines