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Modelling Bulk Elec...
Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning
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- Shao, Yunqi (författare)
- Uppsala universitet,Strukturkemi
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- Knijff, Lisanne (författare)
- Uppsala universitet,Strukturkemi
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- Dietrich, Florian M. (författare)
- Uppsala universitet,Strukturkemi
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- Hermansson, Kersti, Professor (författare)
- Uppsala universitet,Strukturkemi
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- Zhang, Chao (författare)
- Uppsala universitet,Strukturkemi
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(creator_code:org_t)
- 2021-01-04
- 2021
- Engelska.
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Ingår i: Batteries & Supercaps. - : John Wiley & Sons. - 2566-6223. ; 4:4, s. 585-595
- Relaterad länk:
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Kemi -- Fysikalisk kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Physical Chemistry (hsv//eng)
Nyckelord
- Materials Modelling
- Machine Learning
- Neural Network
- Electrolyte
- Interface
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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