CAIML has established two new Special Interest Groups “Materials Science” and “Quantum Physics”.

Materials Science

Machine-learning methods are bridging the gap between macroscopically relevant and quantum-mechanical length scales. Machine learning has the potential to transform materials science by expediting discovery, enhancing prediction capabilities, optimizing processes, enabling data-driven design, and automating analysis tasks. At the same time, known physical laws and constraints can enhance machine learning models by guiding feature engineering, integrating prior knowledge, augmenting data, promoting interpretability, and facilitating model calibration. This SIG will bring together computer scientists and theorists and experimentalist from the sciences to develop powerful and robust models in materials science and identify future directions of research.

Quantum Physics

Machine learning methods open new perspectives on the high-dimensional data arising naturally in complex interacting systems, with applications ranging from analyzing experimental observations over optimal control to enhancing numerical simulations. On the other hand, methods from statistical physics and complex quantum many-body systems theory can improve the understanding of the working principles of modern machine learning methods. This SIG brings together experts to advance the field and identify promising directions for future research.