Theory-guided experimental design in battery materials research

Author:

Eng Alex Yong Sheng1ORCID,Soni Chhail Bihari2ORCID,Lum Yanwei1ORCID,Khoo Edwin3ORCID,Yao Zhenpeng4ORCID,Vineeth S. K.2ORCID,Kumar Vipin2,Lu Jun5ORCID,Johnson Christopher S.5ORCID,Wolverton Christopher6ORCID,Seh Zhi Wei1ORCID

Affiliation:

1. Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore.

2. Department of Energy Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110 016, India.

3. Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis, Singapore 138632, Singapore.

4. The State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, and Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.

5. Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA.

6. Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA.

Abstract

A reliable energy storage ecosystem is imperative for a renewable energy future, and continued research is needed to develop promising rechargeable battery chemistries. To this end, better theoretical and experimental understanding of electrochemical mechanisms and structure-property relationships will allow us to accelerate the development of safer batteries with higher energy densities and longer lifetimes. This Review discusses the interplay between theory and experiment in battery materials research, enabling us to not only uncover hitherto unknown mechanisms but also rationally design more promising electrode and electrolyte materials. We examine specific case studies of theory-guided experimental design in lithium-ion, lithium-metal, sodium-metal, and all-solid-state batteries. We also offer insights into how this framework can be extended to multivalent batteries. To close the loop, we outline recent efforts in coupling machine learning with high-throughput computations and experiments. Last, recommendations for effective collaboration between theorists and experimentalists are provided.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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