The symposium will explore computational predictions and experimental validations, seeking to hasten the practical application of new materials. Contributions are invited across a range of topics, including the discovery of new materials for various energy applications, advanced computational techniques for material behavior and property prediction, and the integration of machine learning and AI for materials discovery. This platform aims to foster innovation and bridge the gap between theoretical research and practical applications in sustainable energy materials.

Suggested topics include, but are not limited to:
- Novel Material Discovery: Computational predictions of new materials with tailored properties for energy applications, spanning photovoltaics, catalysts, batteries, fuel cells, materials for H2 and O2 storage, thermoelectrics, superconductors, and more.
- Simulation and Modeling: Advanced computational techniques (e.g., density functional theory and beyond, interatomic potentials, molecular dynamics) and novel exascale-ready methodologies and computational workflows to simulate and predict the behavior, structure, and properties of energy materials at different scales.
- Experimental-Computational Synergy: Studies showcasing the synergy between computational predictions and experimental validations, highlighting successful transitions from theoretical discoveries to practical applications.
- Materials Design and Optimization: Computational strategies for material design, optimization, and characterization to enhance energy efficiency, durability, and performance.
- Machine Learning in Materials Science: Applications of machine learning and AI in accelerating the discovery and design of energy materials, including data-driven approaches and predictive modeling.