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- W3086873014 abstract "Significance Physical phenomena can often be described by surprisingly few order parameters. Unfortunately, it is challenging to identify these essential degrees of freedom. Here we develop a statistical physics framework for exploring the landscape of order parameters, or coarse-grained representations, for a microscopic protein model. We employ Monte Carlo methods to statistically characterize this landscape. We define metrics assessing the intrinsic quality of each representation for preserving the configurational information and large-scale motions of the underlying microscopic model. Interestingly, these metrics are anticorrelated in low-resolution representations. Moreover, below a critical resolution, a phase transition qualitatively distinguishes superior and inferior representations. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks." @default.
- W3086873014 created "2020-09-21" @default.
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- W3086873014 date "2020-09-14" @default.
- W3086873014 modified "2023-10-12" @default.
- W3086873014 title "Exploring the landscape of model representations" @default.
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- W3086873014 doi "https://doi.org/10.1073/pnas.2000098117" @default.
- W3086873014 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7533877" @default.
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