Symposium Supporters
2024 MRS Fall Meeting & Exhibit
Symposium EN08-Materials Design and Discovery for Next-Generation Energy Storage Systems
This symposium will cover material design and discovery for next-generation energy storage systems. Two major parts will be included: 1) novel synthesis and advanced characterization of energy materials and 2) artificial intelligence (AI) / machine learning (ML) assisted discovery of new materials and mechanism study.
The first part highlights efforts to develop new solid-state materials for next generation battery chemistries and their advanced characterizations related . New superionic materials are critical to enabling stable cycling and safe operation of future high-energy-density electrode materials. Furthermore, developing beyond lithium-ion chemistries based on Na, Zn, K, or Al and other working ions requires developing new electrolyte and electrode materials. Symposium contributions should address the fundamental science and technology for materials design and applications and discuss X-ray, electron- and neutron characterization techniques and approaches for electrochemical energy storage applications.
The second part covers the discovery of novel materials via AI/ML and simulation of interfaces and mechanisms that can aid the adoption of next generation energy storage systems. The ambitious goal of decarbonizing our economy relies on the improvement of renewable energy technology, which require the design, discovery and synthesis of new and sustainable materials. AI and ML provide new approaches for accelerating the availability of new energy storage materials, which enables predictive models from existing material data and establish a new understanding of material behavior, ultimately leading to the development of more cost-effective and high-performance energy storage systems. This symposium will provide state-of-the-art modeling, simulation methods, and complex algorithms that have been developed for energy storage materials. The discussion on interface mechanisms study by AI/ML, the phase diagram for new materials, prediction of their properties and synthesizability, and potential applications will also be extensively included. Abstracts will be solicited in the following areas: design and synthesis of superionic conductors, advanced characterizations on structure/interfaces, new materials beyond lithium battery chemistries, AI/ML applications on mechanism study, and new materials discovery for next-generation energy storage systems.
The first part highlights efforts to develop new solid-state materials for next generation battery chemistries and their advanced characterizations related . New superionic materials are critical to enabling stable cycling and safe operation of future high-energy-density electrode materials. Furthermore, developing beyond lithium-ion chemistries based on Na, Zn, K, or Al and other working ions requires developing new electrolyte and electrode materials. Symposium contributions should address the fundamental science and technology for materials design and applications and discuss X-ray, electron- and neutron characterization techniques and approaches for electrochemical energy storage applications.
The second part covers the discovery of novel materials via AI/ML and simulation of interfaces and mechanisms that can aid the adoption of next generation energy storage systems. The ambitious goal of decarbonizing our economy relies on the improvement of renewable energy technology, which require the design, discovery and synthesis of new and sustainable materials. AI and ML provide new approaches for accelerating the availability of new energy storage materials, which enables predictive models from existing material data and establish a new understanding of material behavior, ultimately leading to the development of more cost-effective and high-performance energy storage systems. This symposium will provide state-of-the-art modeling, simulation methods, and complex algorithms that have been developed for energy storage materials. The discussion on interface mechanisms study by AI/ML, the phase diagram for new materials, prediction of their properties and synthesizability, and potential applications will also be extensively included. Abstracts will be solicited in the following areas: design and synthesis of superionic conductors, advanced characterizations on structure/interfaces, new materials beyond lithium battery chemistries, AI/ML applications on mechanism study, and new materials discovery for next-generation energy storage systems.
Topics will include:
- Novel superionic conductors for Na, K, Zn etc.
- New design of solid electrolytes and their interfaces with electrodes.
- Interfacial characterization to understand the charge transfer.
- Characterizing fast conducting battery materials and interfaces that are challenging for conventional techniques.
- AI/ML-guided energy storage materials design and characterization.
- Advanced simulations of electrochemical interfaces.
Invited Speakers:
- Wurigumula Bao (The University of Chicago, USA)
- Anja Bielefeld (Justus-Liebig-Universität Giessen, Germany)
- Mei Cai (General Motors, USA)
- Rachel Carter (U.S. Naval Research Laboratory, USA)
- Miaofang Chi (Oak Ridge National Laboratory, USA)
- Olivier Delaire (Duke University, USA)
- Betar Gallant (Massachusetts Institute of Technology, USA)
- Rafael Gomez-Bombarelli (Massachusetts Institute of Technology, USA)
- Akitoshi Hayashi (Osaka Prefecture University, Japan)
- Subramanya Herle (Applied Materials, USA)
- Maria K. Chan (Argonne National Laboratory, USA)
- Chen Ling (Toyota Research Institute of North America, USA)
- Lauren Marbella (Columbia University, USA)
- Christian Masquelier (Université de Picardie Jules Verne, France)
- Peter Nellist (University of Oxford, United Kingdom)
- Shyue Ping Ong (University of California, San Diego, USA)
- Tod Pascal (University of California, San Diego, USA)
Symposium Organizers
Y. Shirley Meng
The University of Chicago
USA
Kelsey B. Hatzell
Princeton University
USA
Dan Steingart
Columbia University
USA
Kang Xu
SES AI
USA
Topics
interface
surface chemistry