TY  - JOUR
AU  - Diddens, Diddo
AU  - Appiah, Williams Agyei
AU  - Mabrouk, Youssef
AU  - Heuer, Andreas
AU  - Vegge, Tejs
AU  - Bhowmik, Arghya
TI  - Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?
JO  - Advanced materials interfaces
VL  - 9
IS  - 8
SN  - 2196-7350
CY  - Weinheim
PB  - Wiley-VCH
M1  - FZJ-2022-01222
SP  - 2101734 -
PY  - 2022
AB  - The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on many battery electrodes such as lithium-intercalated graphite or lithium metal anodes. Its essential function is to prevent the electrolyte from continuous electrochemical degradation, while simultaneously allowing ions to pass through, thus constituting an electronically insulating, but ionically conducting material. Its properties crucially affect the overall performance and aging of a battery cell. Despite decades of intense research, understanding the SEI's precise formation mechanism, structure, composition, and evolution remains a conundrum. State-of-the-art computational modeling techniques are powerful tools to gain additional insights, although confronted with a trade-off between accuracy and accessible time- and length scales. In this review, it is discussed how recent advances in data-driven models, especially the development of fast and accurate surrogate simulators and deep generative models, can work with physics-based and physics-informed approaches to enable the next generation of breakthroughs in this field. Machine learning-enhanced multiscale models can provide new pathways to inverse the design of interphases with desired properties.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000749645800001
DO  - DOI:10.1002/admi.202101734
UR  - https://juser.fz-juelich.de/record/906100
ER  -