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How machine learning has helped CREASE
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Classroom Contents
Connecting Modeling, Simulations, and Machine Learning with Experiments for Soft Materials Design - Structure-Property Relationships
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- 1 Intro
- 2 Connecting molecular modeling, simulations, & machine le
- 3 Jayaraman lab studies soft materials polymers, colloids, pa
- 4 Our tools: Molecular modeling, simulations, theory, & machin
- 5 Focus of today's talk
- 6 Structural Characterization of Soft Materials using Small Angl
- 7 Computational Reverse Engineering Analysis of Scattering Ex
- 8 CREASE Step 1: Genetic algorithm (GA)
- 9 How machine learning has helped CREASE
- 10 CREASE for analyzing vesicles' structure
- 11 CREASE vs. SASVIEW fit with core-multi-shell mode vesicles with dispersity in all relevant dimensions
- 12 CREASE: Step 2: Molecular reconstruction within GA informed
- 13 CREASE applied to fibrillar structures in amphiphilic polym
- 14 Methylcellulose and its unique phase behavior in aqueous s
- 15 Dimensions from SAXS data analyzed by CREASE vs. analytical
- 16 CREASE applied to SAXS on synthesized spherical particle
- 17 Predict color for CREASE's reconstructed structure
- 18 'PairVAE' for Pairing Structural Characterization Data Complementary Techniques
