journal article·2023-05-08

Screening Topological Quantum Cathode Materials for K-Ion Batteries by Graph Neural Network and First-Principles Calculations

Yongyi Wang, Jiahui Liu, Peng-Hu Du, Zhili Sun, Qiang Sun

ACS Applied Energy Materials· Volume 6· Issue 9
Publisher page

Abstract

Among the key parts of metal-ion batteries, cathode materials significantly affect the energy density and cycling stability. However, due to the large size of K +, not much progress has been made on cathode materials for K-ion batteries (KIBs). In this study, using the Atomistic Line Graph Neural Network and first-principles calculations, for the first time we screen cathode materials for KIBs from 7385 topological quantum materials with high electronic conductivity and reversible capacity. The experimentally synthesized K 2 MnS 2 is discovered to have a reversible capacity of 203.8 mAh/g, an energy density of 564.5 Wh/kg, a small volume change of 6.4%, and multiple channels for K + transport with fast dynamics. Furthermore, K 2 MnS 2 shows high electrochemical interface stability with the reported solid electrolytes of K 4 V 2 O 7, and K 3 NbP 2 O 9 . These findings suggest that topological quantum materials expand the design space of battery cathodes.
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Research context

OpenAlex topics: Machine Learning in Materials Science · Advancements in Battery Materials · Graphene research and applications

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Affiliations

College of Engineering, Peking University, Beijing 100871, People’s Republic of China

School of Materials Science and Engineering, Peking University, Beijing 100871, People’s Republic of China

Beijing Key Laboratory of Theory and Technology for Advanced Batteries Materials, School of Materials Science and Engineering, Peking University, Beijing 100871, People’s Republic of China

Center for Applied Physics and Technology, Peking University, Beijing 100871, People’s Republic of China

Funding

National Key Research and Development Program of China · National Natural Science Foundation of China

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