Guest Editors:
Dr. Francesco Buonocore, ENEA.
Dr. Massimo Celino, ENEA.
More Details: https://lnkd.in/gBpJVkrR
This Special Issue explores how advanced data platforms and machine learning are accelerating discoveries and applications in the domain of electrochemical storage. We encourage you to submit your contributions presenting established and emerging frameworks illustrating how artificial intelligence and data integration enables breakthroughs in battery design and efficiency optimization. The purpose of this Special Issue is to provide a comprehensive understanding of how to leverage integrated data solutions to push the boundaries of electrochemical storage science. Topics of interest for this Special Issue include, but are not limited to the following:
- Accelerated Materials Discovery and Optimization.
- Battery modelling and digital twins.
- Autonomous Experimentation and Active Learning.
- Standardization, Benchmarking, and Open Data.
- Multi-Scale and Multi-Modal Data Fusion.
- Data platforms for electrochemical storage.
- Fast-Charging and Safety.
- Lifecycle and Degradation Analysis.
If you have any questions, don’t hesitate to get in touch with guest editors.
