Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations (Adv. Energy Mater. 46/2024)

Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations (Adv. Energy Mater. 46/2024)

Autonomous Battery Optimization Autonomous materials acceleration platform involving globally distributed experimental and computational tenants. All tenants are connected to the central fast intention‐agnostic learning server that enforces structured communication, records requests, and results. Ph...

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Veröffentlicht in:Advanced energy materials 2024-12, Vol.14 (46), p.n/a
Hauptverfasser: Vogler, Monika, Steensen, Simon Krarup, Ramírez, Francisco Fernando, Merker, Leon, Busk, Jonas, Carlsson, Johan Martin, Rieger, Laura Hannemose, Zhang, Bojing, Liot, François, Pizzi, Giovanni, Hanke, Felix, Flores, Eibar, Hajiyani, Hamidreza, Fuchs, Stefan, Sanin, Alexey, Gaberšček, Miran, Castelli, Ivano Eligio, Clark, Simon, Vegge, Tejs, Bhowmik, Arghya, Stein, Helge Sören
Format: Artikel
Sprache:eng
Schlagworte:
Artificial intelligence
battery research
Bayesian optimization
decentralized
electrolyte
materials acceleration platform
Optimization
self‐driving laboratory
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Zusammenfassung:Autonomous Battery Optimization Autonomous materials acceleration platform involving globally distributed experimental and computational tenants. All tenants are connected to the central fast intention‐agnostic learning server that enforces structured communication, records requests, and results. Physical inputs and outputs may be manually transferred between the experimental setups to enable workflows with humans, robots, and artificial intelligence in the loop. More in article number 2403263, Arghya Bhowmik, Helge Sören Stein, and co‐workers.
ISSN:1614-6832
1614-6840
DOI:10.1002/aenm.202470204