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Cover Picture: Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning (4/2021)
Author(s) -
Shao Yunqi,
Knijff Lisanne,
Dietrich Florian M.,
Hermansson Kersti,
Zhang Chao
Publication year - 2021
Publication title -
batteries and supercaps
Language(s) - English
Resource type - Reports
ISSN - 2566-6223
DOI - 10.1002/batt.202100061
Subject(s) - electrolyte , schematic , cover (algebra) , representation (politics) , electrochemistry , computer science , electrode , materials science , nanotechnology , chemical physics , chemistry , mechanical engineering , engineering , electrical engineering , politics , political science , law
The Front Cover illustrates the emergence of atomistic machine learning in modelling electrolytes and associated interfaces. The left panel provides a schematic picture of electrode–electrolyte interfaces at the atomic scale. The right panel shows an artistic representation of using crystal structure in artificial intelligence for property predictions. The background reflects the importance of digitalization in electrochemical energy storage. More information can be found in the Minireview by C. Zhang and co‐workers.