High-Dimension Perspective on Extracting & Encoding Information in Chemical Systems

Explore a comprehensive lecture on extracting and encoding high-dimensional information in complex chemical systems. Delve into the opportunities created by exascale computing for simulating chemical behavior with increased realism, including complex mixtures, non-ideality, and extreme conditions. Examine how physical and predictive models benefit from features extracted from simulation data, encoding high-dimensional information in lower-dimensional representations. Investigate this emerging research area's potential for creating holistic information content about chemical systems, encompassing multiscale spatiotemporal correlation and relationships with underlying energy landscapes. Address the challenges of maintaining interpretability, understanding uncertainty, and developing adaptive approaches for feature selection in varying phase spaces. Gain insights from Aurora Clark of the University of Utah, presented at IPAM's workshop on Complex Scientific Workflows at Extreme Computational Scales.