期刊
ADVANCED ENERGY MATERIALS
卷 12, 期 17, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/aenm.202102698
关键词
autonomous discovery; batteries; explainable AI; interface dynamics; multi-sourced multi-scaling
类别
资金
- European Union's Horizon 2020 research and innovation program [957189, 957213]
- Swedish Research Council (Vetenskapsradet)
- Swedish national Strategic e-Science programme eSSENCE
- Slovenian Research Agency [P2-0393, N2-0214]
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2154, 390874152]
- Spanish Ministerio de Ciencia, Innovacion y Universidades [PID2019-107106RB-C33]
- Spanish Agencia Estatal de Investigacion Severo Ochoa Programme for Centres of Excellence in RD [CEX2019-000917-S]
BATTERY 2030+ aims to develop a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries. The AI-assisted toolkits and methodologies developed within the project can be applied to address scientific and technological challenges facing emerging low-TRL battery chemistries and concepts. This includes areas such as predictive simulations, dynamic processes at battery interfaces, and AI-assisted materials characterization.
BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries. Here, a description is given of how the AI-assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low-technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI-assisted toolkit developed within BIG-MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time- and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling and explainable AI, as well as AI-assisted materials characterization, self-driving labs, closed-loop optimization, and AI for advanced sensing and self-healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low-TRL battery chemistries and concepts covering multivalent anodes, metal-sulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries.
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