Accelerated Discovery of Single-Atom Catalysts for Nitrogen Fixation via Machine Learning

Energy & Environmental Materials

article

Sheng Zhang¹  Shuaihua Lu¹  Peng Zhang¹  Jianxiong Tian¹  Li Shi¹  Chongyi Ling¹  Qionghua Zhou¹  Jinlan Wang¹ 

[1] School of Physics, Southeast University

关键词: catalytic descriptor;    electrocatalytic nitrogen reduction;    first-principles calculations;    machine learning;   

DOI  :  10.1002/eem2.12304

来源: Wiley

【 摘 要 】

Developing high-performance catalysts using traditional trial-and-error methods is generally time consuming and inefficient. Here, by combining machine learning techniques and first-principle calculations, we are able to discover novel graphene-supported single-atom catalysts for nitrogen reduction reaction in a rapid way. Successfully, 45 promising catalysts with highly efficient catalytic performance are screened out from 1626 candidates. Furthermore, based on the optimal feature sets, new catalytic descriptors are constructed via symbolic regression, which can be directly used to predict single-atom catalysts with good accuracy and good generalizability. This study not only provides dozens of promising catalysts and new descriptors for nitrogen reduction reaction but also offers a potential way for rapid screening of new electrocatalysts.

【 授权许可】

Unknown