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首页> 外文期刊>Advanced functional materials >Data-Driven High-Throughput Rational Design of Double-Atom Catalysts for Oxygen Evolution and Reduction
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Data-Driven High-Throughput Rational Design of Double-Atom Catalysts for Oxygen Evolution and Reduction

机译：Data-Driven High-Throughput Rational Design of Double-Atom Catalysts for Oxygen Evolution and Reduction

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摘要

Surging interests exist in double-atom catalysts (DACs), which not onlyinherit the advantages of single-atom catalysts (SACs) (e.g., ultimate atomicutilization, high activity, and selectivity) but also overcome the drawbacksof SACs (e.g., low loading and isolated active site). The design of DACs,however, remains cost-prohibitive for both experimental and computationalstudies, due to their huge design space. Herein, by means of densityfunctional theory (DFT) and topological information-based machine-learning(ML) algorithms, we present a data-driven high-throughput design principleto evaluate the stability and activity of 16 767 DACs for oxygen evolution(OER) and oxygen reduction (ORR) reactions. The rational design reveals511 types of DACs with OER activity superior to IrO_2 (110), 855 types ofDACs with ORR activity superior to Pt (111), and 248 bifunctional DACs withhigh catalytic performance for both OER and ORR. An intrinsic descriptoris revealed to correlate the catalytic activity of a DAC with the electronicstructures of the DAC and its bonding carbon surface structure. This datadrivenhigh-throughput approach not only yields remarkable predictionprecision (>0.926 R-squared) but also enables a notable 144 000-foldreduction of screening time compared with pure DFT calculations, holdingpromise to drastically accelerate the design of high-performance DACs.

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