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MachineCat investigates the application of machine learning in computational chemistry to enhance the efficiency of chemical simulations. It aims to demystify machine learning methods and improve their applicability in studying carbon dioxide conversion reactions for sustainable chemistry.
The goal of MachineCat is to obtain fundamental insights into machine learning methods applied to computational chemistry problems.Machine learning methods can be used to reproduce the predictions of highly accurate electronic structure calculations at only a fraction of the original computational cost.
As a consequence, it becomes possible to simulate chemical problems usually beyond the capabilities of standard computational chemistry methods.
However, a routine application of machine learning…
TECHNISCHE UNIVERSITAT BERLIN
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