Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction. (18th May 2022)

• Record Type: Journal Article
• Authors: Wu, Lianping; Guo, Tian; Li, Teng
• Is Part Of: Advanced functional materials. Volume 32:Number 31(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-18
• Subjects: double‐atom catalysts -- machine learning -- oxygen evolution reaction -- oxygen reduction reaction -- single‐atom catalysts; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202203439
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract: Surging interests exist in double‐atom catalysts (DACs), which not only inherit the advantages of single‐atom catalysts (SACs) (e.g., ultimate atomic utilization, high activity, and selectivity) but also overcome the drawbacks of SACs (e.g., low loading and isolated active site). The design of DACs, however, remains cost‐prohibitive for both experimental and computational studies, due to their huge design space. Herein, by means of density functional theory (DFT) and topological information‐based machine‐learning (ML) algorithms, we present a data‐driven high‐throughput design principle to evaluate the stability and activity of 16 767 DACs for oxygen evolution (OER) and oxygen reduction (ORR) reactions. The rational design reveals 511 types of DACs with OER activity superior to IrO2 (110), 855 types of DACs with ORR activity superior to Pt (111), and 248 bifunctional DACs with high catalytic performance for both OER and ORR. An intrinsic descriptor is revealed to correlate the catalytic activity of a DAC with the electronic structures of the DAC and its bonding carbon surface structure. This data‐driven high‐throughput approach not only yields remarkable prediction precision (>0.926 R‐squared) but also enables a notable 144 000‐fold reduction of screening time compared with pure DFT calculations, holding promise to drastically accelerate the design of high‐performance DACs. Abstract : A data‐driven high‐throughput design principle is presented to evaluate the stability and activity of 16 767 double atom catalysts (DACs) for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). The design identifies 511 DACs with OER activity superior to IrO2 (110), 855 DACs with ORR activity superior to Pt (111), and 248 bifunctional DACs with both high OER and ORR activity.