High performing machine learning for novel catalyst design
Description of the granted funding
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.
Show moreStarting year
2022
End year
2024
Granted funding
Funder
Research Council of Finland
Funding instrument
Targeted Academy projects
Other information
Funding decision number
351583
Fields of science
Computer and information sciences
Research fields
Laskennallinen tiede
Identified topics
machines, power engines