Machine Learning Design of Single-Atom Catalysts for Nitrogen Fixation

被引:15
作者
Wang, Shuyue [1 ,2 ]
Qian, Chao [1 ,2 ]
Zhou, Shaodong [1 ,2 ]
机构
[1] Zhejiang Univ, Coll Chem & Biol Engn, Zhejiang Prov Key Lab Adv Chem Engn Manufacture Te, Hangzhou 310027, Peoples R China
[2] Inst Zhejiang Univ Quzhou, Zhejiang Prov Innovat Ctr Adv Chem Technol, Quzhou 324000, Peoples R China
关键词
nitrogen reduction; single-atom catalysts; theoretical design; DFT calculations; machine learning; TOTAL-ENERGY CALCULATIONS; AMMONIA-SYNTHESIS; ACCELERATED DISCOVERY; RATIONAL DESIGN; REDUCTION; ELECTROCATALYSTS; NH3;
D O I
10.1021/acsami.3c08535
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
First-principlescalculations have been combined with machine learningin the design of transition-metal single-atom catalysts. Readily availabledescriptors are selected to describe the nitrogen activation capabilityof metals and coordinating atoms. Thus, a series of V/Nb/Ta-N- x single-atom catalysts are screened out aspromising structures upon considering the stability, activity, andselectivity investigated computationally. Furthermore, by using thegradient boosting regression algorithm, an accurate prediction ofthe hydrogenation barriers for the nitrogen reduction reaction (NRR)is achievable, with a root-mean-squared error of 0.07 eV. The integrationof high-throughput computation and machine learning constitutes apowerful strategy for the acceleration of catalyst design. This approachfacilitates the rapid and accurate prediction of the NRR performanceof more than 1000 single-atom catalyst structures. Moreover, the currentwork provides further insights by elaborately correlating the structureand performance, which may be instructive for both the design andapplication of vanadium-group catalysts.
引用
收藏
页码:40656 / 40664
页数:9
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