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Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations
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DOI：10.1002/aenm.202403263.png)
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Abstract
En 中文
Non-trivial relationships link individual materials properties to device-level performance. Device optimization therefore calls for new automation approaches beyond the laboratory bench with tight integration of different research methods. This study demonstrates a Materials Acceleration Platform (MAP) in the field of battery research based on the problem-agnostic Fast INtention-Agnostic LEarning Server (FINALES) framework, which integrates simulations and physical experiments while leaving the active control of the hardware and software resources executing experiments or simulations with the partners running the respective units. This decentralization of control is a distinctive feature of MAPs using the FINALES framework. The connected capabilities entail the formulation and characterization of electrolytes, cell assembly and testing, early lifetime prediction, and ontology-mapped data storage provided by institutions distributed across Europe. The infrastructure is used to optimize the ionic conductivity of electrolytes and the End Of Life (EOL) of lithium-ion coin cells by varying the electrolyte formulation. Trends in ionic conductivity are rediscovered and the effect of the electrolyte formulation on the EOL is investigated. Further, the capability of this MAP to bridge diverse research modalities, scales, and institutions enabling system-level investigations under asynchronous conditions while handling concurrent workflows on the material- and system-level is shown, demonstrating true intention-agnosticism. A demonstration of a MAP designed for optimizing ionic conductivity and end-of-life by varying the composition of electrolytes consisting of ethylene carbonate (EC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF6) for lithium-ion batteries using the problem agnostic Fast INtention-Agnostic LEarning Server (FINALES) framework. The Materials Acceleration Platform (MAP) is distributed across Europe including autonomous experimental setups, ab initio simulation and machine learning all aiding in the optimization. image
Keywords:
battery research
Bayesian optimization
decentralized
electrolyte
materials acceleration platform
self-driving laboratory
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