Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective

Fichtner, Maximilian; Edström, Kristina; Ayerbe, Elixabete; Berecibar, Maitane; Bhowmik, Arghya; Castelli, Ivano E.; Clark, Simon; Dominko, Robert; Erakca, Merve; Franco, Alejandro A.; Grimaud, Alexis; Horstmann, Birger; Latz, Arnulf; Lorrmann, Henning; Meeus, Marcel; Narayan, Rekha; Pammer, Frank; Ruhland, Janna; Stein, Helge S.; Vegge, Tejs; Weil, Marcel

doi:10.1002/aenm.202102904

Cited by 216 publications

(125 citation statements)

References 301 publications

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“…[ 15,16 ] Breakthroughs have been achieved in protein folding, [ 17 ] drug design, [ 18 ] complex fluid dynamics, [ 19 ] etc. Battery research specifically toward new electrode [ 20–22 ] and electrolyte [ 23–26 ] materials discovery has also gained from these developments, [ 27 ] albeit concrete application on the SEI has not been demonstrated. In this article, we lay out the challenges in the state‐of‐the‐art simulation approaches and examine how the new paradigm of machine learning assisted simulation techniques could help to unravel the fundamental understanding of SEI evolution.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Diddens

Appiah

Mabrouk

et al. 2022

Adv Materials Inter

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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.

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Section: Introductionmentioning

confidence: 99%

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Diddens

Appiah

Mabrouk

et al. 2022

Adv Materials Inter

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“…In this section, we review the current status of sensors and sensing activities within the battery field to identify the remaining scientific, technological, and systemic challenges (see also Ref. [ 3 ] in this issue). Strategies to alleviate them are discussed and highlighted with the ultimate goal of creating highly reliable batteries with ultra‐high performance and long life.…”

Section: Battery 2030+: Research Areasmentioning

confidence: 99%

“…This paper summarizes the roadmap developed by the always BATTERY 2030+ consortium and is complemented by a number of articles in this special issue, including also one paper regarding the state‐of‐the‐art. [ 2–11 ]…”

Section: Introductionmentioning

confidence: 99%

A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+

Amici

Asinari

Ayerbe

et al. 2022

Advanced Energy Materials

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This roadmap presents the transformational research ideas proposed by “BATTERY 2030+,” the European large‐scale research initiative for future battery chemistries. A “chemistry‐neutral” roadmap to advance battery research, particularly at low technology readiness levels, is outlined, with a time horizon of more than ten years. The roadmap is centered around six themes: 1) accelerated materials discovery platform, 2) battery interface genome, with the integration of smart functionalities such as 3) sensing and 4) self‐healing processes. Beyond chemistry related aspects also include crosscutting research regarding 5) manufacturability and 6) recyclability. This roadmap should be seen as an enabling complement to the global battery roadmaps which focus on expected ultrahigh battery performance, especially for the future of transport. Batteries are used in many applications and are considered to be one technology necessary to reach the climate goals. Currently the market is dominated by lithium‐ion batteries, which perform well, but despite new generations coming in the near future, they will soon approach their performance limits. Without major breakthroughs, battery performance and production requirements will not be sufficient to enable the building of a climate‐neutral society. Through this “chemistry neutral” approach a generic toolbox transforming the way batteries are developed, designed and manufactured, will be created.

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“…For a detailed description of the current state of the art, please refer to SOA Fichtner et al in this issue. [41] While generative models can help create large structure libraries autonomously from smaller ones, screening processes to search for candidates that satisfy property requirements are needed during the design process. Alternatively, true inverse design can be achieved with conditional generative models that learn the probability distribution in structural space conditioned to the properties.…”

Section: Generative Models and Inverse Designmentioning

confidence: 99%

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Bhowmik¹,

Berecibar

Casas‐Cabanas

et al. 2021

Advanced Energy Materials

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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, metalsulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries.

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Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective

Cited by 216 publications

References 301 publications

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

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