GSOFT Short Course: Machine Learning and Data Science in Soft Matter

Data-driven modeling approaches and machine learning have opened new paradigms in the understanding, engineering, and design of soft and biological materials. The advent of high-throughput experimental synthesis and characterization platforms, and the increasing prevalence of high-performance and multicore computer hardware have led to a deluge of data in soft matter. Analysis of these voluminous and multidimensional data sets requires soft matter researchers to implement and adapt tools from machine learning and data science. This one-day workshop will provide emerging and established soft matter researchers with exposure and training in machine learning and data science tools through a series of tutorials from some of the leading experts in the field. Topics to be covered include nonlinear manifold learning, enhanced sampling, materials informatics, and inverse soft materials design. Attendees will leave with both an appreciation for the state-of-the-art applications of data science in soft matter research, and a working knowledge of user-friendly Python libraries to implement these approaches in their own work.