Journal article
Coarse-graining errors and numerical optimization using a relative entropy framework
The Journal of chemical physics, v 134(9), 094112
07 Mar 2011
PMID: 21384955
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The ability to generate accurate coarse-grained models from reference fully atomic (or otherwise "first-principles") ones has become an important component in modeling the behavior of complex molecular systems with large length and time scales. We recently proposed a novel coarse-graining approach based upon variational minimization of a configuration-space functional called the relative entropy, S(rel), that measures the information lost upon coarse-graining. Here, we develop a broad theoretical framework for this methodology and numerical strategies for its use in practical coarse-graining settings. In particular, we show that the relative entropy offers tight control over the errors due to coarse-graining in arbitrary microscopic properties, and suggests a systematic approach to reducing them. We also describe fundamental connections between this optimization methodology and other coarse-graining strategies like inverse Monte Carlo, force matching, energy matching, and variational mean-field theory. We suggest several new numerical approaches to its minimization that provide new coarse-graining strategies. Finally, we demonstrate the application of these theoretical considerations and algorithms to a simple, instructive system and characterize convergence and errors within the relative entropy framework.
Metrics
Details
- Title
- Coarse-graining errors and numerical optimization using a relative entropy framework
- Creators
- Aviel Chaimovich - University of California, Santa BarbaraM Scott Shell - University of California, Santa Barbara
- Publication Details
- The Journal of chemical physics, v 134(9), 094112
- Publisher
- American Institute of Physics (AIP)
- Number of pages
- 15
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000288085300014
- Scopus ID
- 2-s2.0-79952512739
- Other Identifier
- 991021010835604721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Chemistry, Physical
- Physics, Atomic, Molecular & Chemical