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1
Intro
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The necessity of coarse-graining
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Challenges in coarse-graining biomolecular systems
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Why use machine learning in this setting?
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Invertible, state-dependent coarse-graining
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Flexible and useful embeddings
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Thermodynamic consistency (Noid and Voth)
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Weak thermodynamic consistency
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Rigorously inverting the CG sampling
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Necessary Sacrifices
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Metastability in protein folding / misfolding
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Large-and small-scale observables well-captured
Description:
Explore a seminar on weak convergence and invertible coarse-graining in probability theory and molecular dynamics. Delve into Prof. Grant M. Rotskoff's discussion of combining force-matching based coarse-graining with invertible neural networks to invert coarse-graining maps statistically. Learn about the thermodynamic equivalence principle, challenges in coarse-graining biomolecular systems, and the application of machine learning in this context. Discover how this approach can recover fine-grained free energy surfaces from coarse-grained sampling in non-trivial biomolecular systems. Gain insights into metastability in protein folding/misfolding and the capture of large- and small-scale observables through this innovative method.

A Weak Convergence Viewpoint on Invertible Coarse-Graining

Cambridge Materials
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