Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling

Nature·Dec 19

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Research is being conducted on the use of machine learning to model catalytic reactivity, with a focus on improving efficiency and accuracy. Various studies are exploring the application of machine learning potentials in heterogeneous catalysis, including the use of active learning, enhanced sampling, and transfer learning. The goal of this research appears to be the development of more effective catalyst materials, with potential applications in fields such as energy.

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Wiley Online Library·Feb 17

Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis

ACS Publications·Sep 11

Fast and Fourier features for transfer learning of interatomic potentials

Nature·Sep 25

Catalysis in the digital age: Unlocking the power of data with machine learning

Wiley Interdisciplinary Reviews·Sep 20

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Nature·Apr 29

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Nature·Mar 24

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