High performing machine learning for novel catalyst design (MLNovCat)
Table of contents
Project description
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 Ä¢¹½Ö±²¥ that have demonstrated complementary expertise in the computational catalysis, materials science, and computational science.
