Solving our society's issues in energy, water, environment, and health necessitates discovery of new materials and chemistry. Establishing structure-property relationships is key to realizing materials' functions for renewable energy, clean water and environment, and biomedical applications. Advances in theoretical understanding, algorithms, computational power, big data, artificial intelligence and machine learning are propelling computational tools to an increasing role in materials design, discovery, development, and optimization. One important goal is to achieve predictive modeling of materials synthesis, property, function, evolution, and lifecycle.

This workshop aims to bring together computational scientists working on focused topics of materials chemistry to exchange ideas and to stimulate discussion. The 2025 workshop will concentrate on accelerated transition-state search and reaction mechanism discovery. Topics include, but not limited to, new methods to locate transition states, efficient exploration of potential energy surface, automated workflow, catalysis and reaction networks, and solid-state reactions and transitions. This workshop will provide a unique opportunity for the participants to broaden their view and deepen their understanding of important issues, challenges, and recent progress toward predictive modeling of chemistry of materials.