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 2023 workshop will concentrate on predictive modeling of reactions in condensed phases that underlie materials synthesis, property, function, and evolution. Topics include, but are not limited to, solvation effects and models, sampling and free-energy simulations, catalysis in liquid phases, and interfacial phenomena. 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.