High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research
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- Anass Benayad
- Univ. Grenoble Alpes CEA Liten Grenoble 38000 France
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- Diddo Diddens
- Forschungszentrum Jülich GmbH Helmholtz‐Institute Münster (IEK‐12) Corrensstrasse 46 48149 Münster Germany
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- Andreas Heuer
- Forschungszentrum Jülich GmbH Helmholtz‐Institute Münster (IEK‐12) Corrensstrasse 46 48149 Münster Germany
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- Anand Narayanan Krishnamoorthy
- Forschungszentrum Jülich GmbH Helmholtz‐Institute Münster (IEK‐12) Corrensstrasse 46 48149 Münster Germany
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- Moumita Maiti
- MEET Battery Research Center University of Münster Corrensstrasse 46 48149 Münster Germany
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- Frédéric Le Cras
- Univ. Grenoble Alpes CEA Liten Grenoble 38000 France
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- Maxime Legallais
- Univ. Grenoble Alpes CEA CTREG DNAQ Pessac 33600 France
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- Fuzhan Rahmanian
- Helmholtz Institute Ulm Helmholtzstrasse 11 89081 Ulm Germany
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- Yuyoung Shin
- Helmholtz Institute Ulm Helmholtzstrasse 11 89081 Ulm Germany
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- Helge Stein
- Helmholtz Institute Ulm Helmholtzstrasse 11 89081 Ulm Germany
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- Martin Winter
- Forschungszentrum Jülich GmbH Helmholtz‐Institute Münster (IEK‐12) Corrensstrasse 46 48149 Münster Germany
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- Christian Wölke
- Forschungszentrum Jülich GmbH Helmholtz‐Institute Münster (IEK‐12) Corrensstrasse 46 48149 Münster Germany
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- Peng Yan
- Forschungszentrum Jülich GmbH Helmholtz‐Institute Münster (IEK‐12) Corrensstrasse 46 48149 Münster Germany
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- Isidora Cekic‐Laskovic
- Forschungszentrum Jülich GmbH Helmholtz‐Institute Münster (IEK‐12) Corrensstrasse 46 48149 Münster Germany
抄録
<jats:title>Abstract</jats:title><jats:p>The timely arrival of novel materials plays a key role in bringing advances to 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 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 toward 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, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces.</jats:p>
収録刊行物
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- Advanced Energy Materials
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Advanced Energy Materials 12 (17), 2021-11-09
Wiley