High performing machine learning for novel catalyst design

Bidragets beskrivning

Cleanly produced hydrogen, which can be produced through water electrocatalysis, is crucial for achieving a low-carbon society. Novel, next-generation catalysts for this reaction can be based on small monolayer-protected clusters (MPCs), which contain multiple tunable properties. To speed up their design, high performing and reliable data-driven methods utilizing graphics processing units (GPU) should be applied. In the project, a new concept for the design of catalysts is created, which can replace the conventional trial-and-error experimental laboratory work. The consortium for the project is interdisciplinary, consisting of three groups at the University of Jyväskylä that have demonstrated complementary expertise in the computational catalysis, materials science, and computational science.
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Startår

2022

Slutår

2024

Beviljade finansiering



Hannu Häkkinen Orcid -palvelun logo
228 709 €

Rollen i Finlands Akademis konsortium

Övriga parter i konsortiet

Leader
Jyväskylä universitet (351579)
354 112 €
Partner
Jyväskylä universitet (351583)
230 664 €

Finansiär

Finlands Akademi

Typ av finansiering

Akademiprojekt med särskild inriktning

Övriga uppgifter

Finansieringsbeslutets nummer

351582

Vetenskapsområden

Data- och informationsvetenskap

Forskningsområden

Laskennallinen tiede

Identifierade teman

machines, power engines