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.
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Startår

2026

Slutår

2030

Beviljade finansiering

Ransell DSouza Orcid -palvelun logo
699 800 €

Finansiär

Finlands Akademi

Typ av finansiering

Akademiforskare

Beslutfattare

Forskningsrådet för naturvetenskap och teknik
09.06.2026

Övriga uppgifter

Finansieringsbeslutets nummer

376677

Vetenskapsområden

Fysik

Forskningsområden

Tiiviin aineen fysiikka

Identifierade teman

machines, power engines