期刊
BATTERIES & SUPERCAPS
卷 4, 期 4, 页码 585-595出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/batt.202000262
关键词
Materials Modelling; Machine Learning; Neural Network; Electrolyte; Interface
资金
- Swedish Research Council (VR) [2019-05012, 2019-04824]
- Swedish National Strategic e-Science program eSSENCE
- Centre for Interdisciplinary Mathematics (CIM) at Uppsala University
- Swedish Research Council [2019-05012, 2019-04824] Funding Source: Swedish Research Council
- Vinnova [2019-05012] Funding Source: Vinnova
Batteries and supercapacitors are electrochemical energy storage systems that require a molecular-level understanding of electrolytes for optimizing performance and ensuring safety. Atomistic machine learning is a promising technology for bridging microscopic models and macroscopic phenomena.
Batteries and supercapacitors are electrochemical energy storage systems which involve multiple time-scales and length-scales. In terms of the electrolyte which serves as the ionic conductor, a molecular-level understanding of the corresponding transport phenomena, electrochemical (thermal) stability and interfacial properties is crucial for optimizing the device performance and achieving safety requirements. To this end, atomistic machine learning is a promising technology for bridging microscopic models and macroscopic phenomena. Here, we provide a timely snapshot of recent advances in this area. This includes technical considerations that are particularly relevant for modelling electrolytes as well as specific examples of both bulk electrolytes and associated interfaces. A perspective on methodological challenges and new applications is also discussed.
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