Machine learning in catalysis

By John R. Kitchin. Nature Catalysis, 2018

388 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Catalysis, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/f4bfd0add23a9a8e06f5b1ac6780e713dc9d4eff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhen Zhou
2. An Chen
3. Xu Zhang

In Catalysis

1. X. Tu
2. Zhen Zhou
3. An Chen
4. Xu Zhang
5. Annemie Bogaerts

In Materials Chemistry

1. Annemie Bogaerts
2. X. Tu
3. J. Whitehead
4. Xu Zhang
5. Hyun‐Ha Kim
6. G. Centi
7. Zhen Zhou
8. An Chen

Citations in the corpus

• Machine learning: Accelerating materials development for energy storage and conversion (2020, methodology): “Recently, researchers have integrated ML and QM methods to overcome the limitations of pure QM methods, accelerating the accurate screening of catalysts.(24,33) For example, QM methods are computationally expensive and therefore limit the applications to largescale complex systems.”
• The 2020 plasma catalysis roadmap (2020, methodology): “ML is being applied to develop structure-function relationships and even as a surrogate for DFT calculations at the molecular scale [33, 108].”