Master Class: Data Science for SCCM

The modeling of energetic materials presents unique challenges due to their complex chemistry, hazardous nature, and the need for accurate property predictions across various conditions. Machine learning (ML) has emerged as a transformative tool, offering powerful methods to analyze and predict the behavior of these materials, enabling safer and more efficient designs. This masterclass explores the state-of-the-art applications of ML in the field of energetic materials, bridging theoretical principles with practical applications.

Key topics include data-driven property prediction, such as detonation velocity, enthalpy of formation, and sensitivity; the use of molecular descriptors in ML models; and the integration of high-throughput screening platforms. The masterclass is designed for researchers, engineers, and industry professionals aiming to harness ML to revolutionize energetic material design and testing. Participants will leave with a comprehensive understanding of ML's capabilities and practical skills to implement robust, predictive models tailored to the demands of this specialized field.