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High throughput experimentation and computational freeway lanes for accelerated battery electrolyte & interface development research

Abstract : The timely arrival of novel materials plays a simultaneous key and critical role in bringing advances to our society as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at which novel materials are identified within the chemical space. High throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes towards identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning (ML), deep learning (DL) and artificial intelligence (AI). This Review aims to highlight state-of-the-art achievements along with an assessment of current and future challenges as well as resulting perspectives towards accelerated development of advanced battery electrolytes and their interfaces.
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Submitted on : Thursday, November 25, 2021 - 9:37:59 AM
Last modification on : Friday, November 26, 2021 - 3:45:44 AM

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Maxime Legallais, Anass Benayad, Isidora Cekic-Laskovic, Christian Wölke, Martin Winter, et al.. High throughput experimentation and computational freeway lanes for accelerated battery electrolyte & interface development research. Advanced Energy Materials, Wiley-VCH Verlag, 2021, pp.10.1002/aenm.202102678. ⟨10.1002/aenm.202102678⟩. ⟨cea-03448258⟩

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