An end-to-end framework for reactivity in heterogeneous catalysis

Nature·Mar 19

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Researchers are developing frameworks and methods to improve understanding and application of heterogeneous catalysis, a process used in chemical synthesis. Machine learning and computer vision are being leveraged to analyze and optimize catalytic reactions, including the discovery of active sites and the design of catalysts. The work appears to have potential applications in various fields, including the production of specialty chemicals, pharmaceuticals, and recycling of plastics.

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Visualizing nexus of porous architecture and reactive transport in heterogeneous catalysis by deep learning computer vision and transfer learning

Nature·Aug 29

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

ACS Publications·Sep 11

Advances in solid handling for continuous flow synthesis of specialty chemicals and pharmaceuticals

Nature·Feb 23

Toward a Circular Economy of Heteroatom Containing Plastics: A Focus on Heterogeneous Catalysis in Recycling

ACS Publications·Mar 3

Processive ring-opening metathesis polymerization of low ring strain cycloalkenes via molecularly confined catalyst

Nature·Oct 1

Machine-learning atomic simulation for heterogeneous catalysis | npj Computational Materials

Nature·Jan 7

Machine learning accelerated descriptor design for catalyst discovery in CO

Nature·Jul 4

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

Nature·Dec 19

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