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.
Visa merStartår
2022
Slutår
2024
Beviljade finansiering
Rollen i Finlands Akademis konsortium
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