High-Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research

Autor: Anass Benayad, Fuzhan Rahmanian, Diddo Diddens, Martin Winter, Christian Wölke, Yuyoung Shin, Peng Yan, Helge S. Stein, Andreas Heuer, Frédéric Le Cras, Anand Narayanan Krishnamoorthy, Maxime Legallais, Isidora Cekic-Laskovic, Moumita Maiti
Přispěvatelé: Département des Technologies des NanoMatériaux (DTNM), Laboratoire d'Innovation pour les Technologies des Energies Nouvelles et les nanomatériaux (LITEN), Institut National de L'Energie Solaire (INES), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de L'Energie Solaire (INES), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Institute of Physical Chemistry & MEET Battery Research Centre, Département de l'électricité et de l'hydrogène dans les transports (DEHT), Helmholtz Institute Ulm (HIU), European Project: 957189,BIG-MAP, European Project: 957213,BATTERY 2030PLUS, Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de L'Energie Solaire (INES), Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Advanced Energy Materials, 12 (17), Art.Nr.: 2102678
Advanced energy materials 12(17), 2102678-(2021). doi:10.1002/aenm.202102678
Advanced Energy Materials
Advanced Energy Materials, 2021, 2021, pp.2102678. ⟨10.1002/aenm.202102678⟩
Advanced Energy Materials, Wiley-VCH Verlag, 2021, 2021, pp.2102678. ⟨10.1002/aenm.202102678⟩
ISSN: 1614-6832
1614-6840
Popis: International audience; 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.
Databáze: OpenAIRE
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