| Abstract Scope |
Accelerating the discovery of sustainable materials is critical for next-generation energy infrastructure and supply chain resilience. This presentation explores the convergence of artificial intelligence, advanced spectroscopy, and physics-based modeling to close the gap between fundamental design and industrial scalability.
Drawing on case studies in energy storage—including redox flow chemistries and solid-state electrolytes—we will demonstrate how inverse design frameworks can predict complex molecular behaviors before synthesis. Specifically, we will highlight the use of multimodal data integration to unveil inherent structures and predict performance of energy storage materials. These methodologies illustrate a paradigm shift from trial-and-error experimentation to rational, AI-guided inverse design, offering a blueprint for deploying high-performance materials at the speed and scale required by modern industry. |
