Computational chemistry has the potential to aid in the design of heterogeneous catalysts; however, there is currently a large gap between the complexity of real systems and what can be readily computed at scale. This Review discusses the ways in which machine learning can assist in closing this gap to facilitate rapid advances in catalyst discovery.
- Tianyou Mou
- Hemanth Somarajan Pillai
- Hongliang Xin
