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The ML4Catalysis project aims to enhance the design of homogeneous catalysts using machine learning techniques. It focuses on automating quantum chemistry calculations, improving accuracy beyond traditional methods, and generating new catalysts for CO2 hydrogenation, which is vital for sustainable fuel production.
While machine learning (ML) methods are already commonly applied in heterogeneous catalysis, the use of such methods for the design of homogeneous catalysts is a largely overlooked field.
A recent proof-of-principle study showed the huge potential of ML in homogeneous catalysis by demonstrating that activation barriers in a set of related transition metal (TM) complexes can be learned.
ML4Catalysis has three objectives that go far beyond this state of the art:1) Automation of quantum chemistry (…
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