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.
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Starting year

2022

End year

2024

Granted funding



Hannu Häkkinen Orcid -palvelun logo
228 709 €

Funder

Research Council of Finland

Funding instrument

Targeted Academy projects

Other information

Funding decision number

351582

Fields of science

Computer and information sciences

Research fields

Laskennallinen tiede

Identified topics

machines, power engines