A Database of Electrochemical Stability Windows Containing over 1500 Solid‐State Inorganic Compounds

Author:

Wang Xianyue1,He Bing2,Liu Bo3,Avdeev Maxim45,Shi Siqi167ORCID

Affiliation:

1. School of Materials Science and Engineering Shanghai University Shanghai 200444 China

2. School of Computer Engineering and Science Shanghai University Shanghai 200444 China

3. College of Mathematics and Physics Jinggangshan University Ji'an 343009 China

4. Australian Nuclear Science and Technology Organisation Kirrawee DC NSW 2232 Australia

5. School of Chemistry The University of Sydney Sydney 2006 Australia

6. Materials Genome Institute Shanghai University Shanghai 200444 China

7. Zhejiang Laboratory Hangzhou 311100 China

Abstract

AbstractElectrochemical stability window (ESW) of an inorganic compound (solid‐state electrolyte (SSE) or coating) is an indispensable parameter to evaluate the interface compatibility between the electrode and electrolyte in ion batteries. The discovery of novel coatings and SSEs, driven by extensive ESW data, is expected to accelerate the design of high‐performance batteries. However, only a very small fraction of the ESW of inorganic compounds has been experimentally measured at present, which limits technological progress. Benefiting from the high‐accuracy ESW prediction with dynamically determined direct or indirect decomposition pathway proposed in the previous work, both the oxidation and reduction potentials align to the experimental data reasonably. Here, a database containing phase diagrams and electrochemical stability information is established for more than 1500 solid‐state inorganic compounds with Li+, Na+, K+, Mg2+, Ca2+, and Al3+ as the migrating ions, and this number is still growing. The database is reproducible and provides a unified picture of the structure–activity relationships associated with electrochemical stability of inorganic compounds. This study demonstrates the validity of the improved ESW prediction method and paves the way for accelerated screening of superior SSEs or coatings based on machine learning.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province

Natural Science Foundation of Shandong Province

Publisher

Wiley

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