Accelerated Discovery of Single‐Atom Catalysts for Nitrogen Fixation via Machine Learning. Issue 1 (8th April 2022)

• Record Type: Journal Article
• Authors: Zhang, Sheng; Lu, Shuaihua; Zhang, Peng; Tian, Jianxiong; Shi, Li; Ling, Chongyi; Zhou, Qionghua; Wang, Jinlan
• Is Part Of: Energy & environmental materials. Volume 6:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-08
• Subjects: catalytic descriptor -- electrocatalytic nitrogen reduction -- first‐principles calculations -- machine learning; Power resources -- Environmental aspects -- Periodicals; Renewable energy sources -- Periodicals; Environmental engineering -- Periodicals; 333.79
• DOI: 10.1002/eem2.12304
• Languages: English
• ISSNs: 2575-0356

Abstract

Abstract : 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. Abstract : Machine learning (ML) accelerates the discovery of single‐atom catalysts for nitrogen fixation. Through data preparation, ML prediction, screening, and DFT validation, efficient NRR catalysts were selected.