TY - JOUR
T1 - Self-Validated Machine Learning Study of Graphdiyne-Based Dual Atomic Catalyst
AU - Sun, Mingzi
AU - Wu, Tong
AU - Dougherty, Alan William
AU - Lam, Maggie
AU - Huang, Bolong
AU - Li, Yuliang
AU - Yan, Chun Hua
N1 - Funding Information:
The authors gratefully acknowledge the support of the Natural Science Foundation of China (grant no. NSFC 21771156) and the Early Career Scheme (ECS) fund (grant no. PolyU 253026/16P) from the Research Grant Council (RGC) in Hong Kong.
Publisher Copyright:
© 2021 Wiley-VCH GmbH
PY - 2021/4/8
Y1 - 2021/4/8
N2 - Although the atomic catalyst has attracted intensive attention in the past few years, the current progress of this field is still limited to a single atomic catalyst (SAC). With very few successful cases of dual atomic catalysts (DACs), the most challenging part of experimental synthesis still lies in two main directions: the thermodynamic stability of the synthesis and the optimal combination of metals. To address such challenges, comprehensive theoretical investigations on graphdiyne (GDY)-based DAC are proposed by considering both, the formation stability and the d-band center modifications. Unexpectedly, it is proven that the introduction of selected lanthanide metals to the transition metals contributes to the optimized stability and electroactivity. With further verification by machine learning, the potential f–d orbital coupling is unraveled as the pivotal factor in modulating the d-band center with enhanced stability by less orbital repulsive forces. These findings supply the delicate explanations of the atomic interactions and screen out the most promising DAC to surpass the limitations of conventional trial and error synthesis. This work has supplied an insightful understanding of DAC, which opens up a brand new direction to advance the research in atomic catalysts for broad applications.
AB - Although the atomic catalyst has attracted intensive attention in the past few years, the current progress of this field is still limited to a single atomic catalyst (SAC). With very few successful cases of dual atomic catalysts (DACs), the most challenging part of experimental synthesis still lies in two main directions: the thermodynamic stability of the synthesis and the optimal combination of metals. To address such challenges, comprehensive theoretical investigations on graphdiyne (GDY)-based DAC are proposed by considering both, the formation stability and the d-band center modifications. Unexpectedly, it is proven that the introduction of selected lanthanide metals to the transition metals contributes to the optimized stability and electroactivity. With further verification by machine learning, the potential f–d orbital coupling is unraveled as the pivotal factor in modulating the d-band center with enhanced stability by less orbital repulsive forces. These findings supply the delicate explanations of the atomic interactions and screen out the most promising DAC to surpass the limitations of conventional trial and error synthesis. This work has supplied an insightful understanding of DAC, which opens up a brand new direction to advance the research in atomic catalysts for broad applications.
KW - dual-atomic catalysts
KW - f–d orbital couplings
KW - graphdiyne
KW - machine learning
KW - self-validation
UR - http://www.scopus.com/inward/record.url?scp=85100864146&partnerID=8YFLogxK
U2 - 10.1002/aenm.202003796
DO - 10.1002/aenm.202003796
M3 - Journal article
AN - SCOPUS:85100864146
SN - 1614-6832
VL - 11
JO - Advanced Energy Materials
JF - Advanced Energy Materials
IS - 13
M1 - 2003796
ER -