Implications of the BATTERY 2030+ AI-Assisted Toolkit on Future Low-TRL Battery Discoveries and Chemistries
Authors
Bhowmik, A
Berecibar, M
Casas-Cabanas, M
Dominko, R
Hermansson, K
Palacin, MR
Stein, HS
Publication Date
2022-05Journal Title
Advanced Energy Materials
ISSN
1614-6832
Publisher
Wiley
Language
en
Type
Article
This Version
AO
VoR
Metadata
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Bhowmik, A., Berecibar, M., Casas-Cabanas, M., Csanyi, G., Dominko, R., Hermansson, K., Palacin, M., et al. (2022). Implications of the BATTERY 2030+ AI-Assisted Toolkit on Future Low-TRL Battery Discoveries and Chemistries. Advanced Energy Materials https://doi.org/10.1002/aenm.202102698
Description
Funder: Swedish national Strategic e‐Science programme
Funder: Deutsche Forschungsgemeinschaft; Id: http://dx.doi.org/10.13039/501100001659
Abstract
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.
Keywords
Review, Reviews, autonomous discovery, batteries, explainable AI, interface dynamics, multi‐sourced multi‐scaling
Sponsorship
European Commission Horizon 2020 (H2020) Research Infrastructures (RI) (957189)
Identifiers
aenm202102698
External DOI: https://doi.org/10.1002/aenm.202102698
This record's URL: https://www.repository.cam.ac.uk/handle/1810/331016
Rights
Licence:
http://creativecommons.org/licenses/by-nc/4.0/
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