Connecting Modeling, Simulations, and Machine Learning with Experiments for Soft Materials Design - Structure-Property Relationships

Explore a virtual seminar presented by Prof. Arthi Jayaraman from the University of Delaware, focusing on connecting modeling, simulations, and machine learning with experiments for design-structure-property relationships in soft materials. Delve into the Jayaraman lab's study of soft materials, including polymers and colloids, using molecular modeling, simulations, theory, and machine learning tools. Learn about the Computational Reverse Engineering Analysis of Scattering Experiments (CREASE) method, its application in structural characterization of soft materials using Small Angle X-ray Scattering (SAXS), and how machine learning enhances this process. Examine case studies on vesicles, fibrillar structures in amphiphilic polymers, methylcellulose's unique phase behavior in aqueous solutions, and synthesized spherical particles. Discover the 'PairVAE' technique for pairing structural characterization data from complementary techniques, and gain insights into predicting color for CREASE's reconstructed structures.