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- W2340852163 abstract "Generating and calibrating forces that are transferable across a range of state-points remains a challenging task in coarse-grained (CG) molecular dynamics. In this work, we present a coarse-graining workflow, inspired by ideas from uncertainty quantification and numerical analysis, to address this problem. The key idea behind our approach is to introduce a Bayesian correction algorithm that uses functional derivatives of CG simulations to rapidly and inexpensively recalibrate initial estimates f0 of forces anchored by standard methods such as force-matching. Taking density-temperature relationships as a running example, we demonstrate that this algorithm, in concert with various interpolation schemes, can be used to efficiently compute physically reasonable force curves on a fine grid of state-points. Importantly, we show that our workflow is robust to several choices available to the modeler, including the interpolation schemes and tools used to construct f0. In a related vein, we also demonstrate that our approach can speed up coarse-graining by reducing the number of atomistic simulations needed as inputs to standard methods for generating CG forces." @default.
- W2340852163 created "2016-06-24" @default.
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- W2340852163 date "2016-04-15" @default.
- W2340852163 modified "2023-10-16" @default.
- W2340852163 title "Bayesian calibration of coarse-grained forces: Efficiently addressing transferability" @default.
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- W2340852163 doi "https://doi.org/10.1063/1.4945380" @default.
- W2340852163 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5087108" @default.
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