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.

interface surface chemistry