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- Castelli, Ivano E., et al.
(författare)
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Data Management Plans : the Importance of Data Management in the BIG-MAP Project
- 2021
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Ingår i: Batteries & Supercaps. - : John Wiley & Sons. - 2566-6223. ; 4:12, s. 1803-1812
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Tidskriftsartikel (refereegranskat)abstract
- Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EU H2020 BIG-MAP project-a cross-disciplinary project targeting disruptive battery-material discoveries. Essential for reaching the goal is extensive sharing of research data across scales, disciplines and stakeholders, not limited to BIG-MAP and the European BATTERY 2030+ initiative but within the entire battery community. The key challenges faced in developing the data management plan for such a large and complex project were to generate an overview of the enormous amount of data that will be produced, to build an understanding of the data flow within the project and to agree on a roadmap for making all data FAIR (findable, accessible, interoperable, reusable). This paper describes the process we followed and how we structured the plan.
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| 2. |
- Vegge, Tejs, et al.
(författare)
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Toward Better and Smarter Batteries by Combining AI with Multisensory and Self-Healing Approaches
- 2021
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Ingår i: Advanced Energy Materials. - : John Wiley & Sons. - 1614-6832 .- 1614-6840. ; 11:23
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Tidskriftsartikel (refereegranskat)abstract
- With an exponentially growing demand for rechargeable batteries, the development of new ultra-performant, fully scalable, and sustainable battery technologies and materials must be accelerated. Creating a holistic, closed-loop infrastructure for materials discovery, manufacturing, and battery testing that utilizes a common data infrastructure and autonomous workflows to bridge big data from all domains of the battery value chain, can pave the way for a transformative reduction in the required time to discovery. By embedding multisensory and self-healing capabilities in future battery technologies and integrating these with AI and physics-aware machine learning models capable of predicting the spatio-temporal evolution of battery materials and interfaces, it will, in time, be possible to identify, predict and prevent potential degradation and failure modes. This will facilitate enhanced battery quality, reliability, and life, for example, by preemptively changing the battery charging conditions or releasing self-healing additives from the separator membrane, akin to preemptive medicine, and form the basis for inverse design of new battery materials, interfaces, and additives. The large-scale and long-term European research initiative BATTERY 2030+ seeks to make this longer-than ten-year vision a reality through the development of a versatile and chemistry neutral "Battery Interface Genome-Materials Acceleration Platform" infrastructure (BIG-MAP).
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