Machine learning for heterogeneous catalyst design and discovery

By Bryan R. Goldsmith, J. Esterhuizen, Jin-Xun Liu, Christopher J. Bartel, Christopher Sutton. 2018

369 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanical Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/8d41cf63e1455b27fad5b9299c49aaa658c0cd85

How researchers see this paper

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

In Materials Chemistry

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

In Mechanical Engineering

1. Zhen Zhou

In Renewable Energy, Sustainability and the Environment

1. An Chen
2. Xu Zhang

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.”