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- W2952473833 abstract "Despite more than 40 years of research in condensed-matter physics, state-of-the-art approaches for simulating the radial distribution function (RDF) g(r) still rely on binning pair-separations into a histogram. Such methods suffer from undesirable properties, including subjectivity, high uncertainty, and slow rates of convergence. Moreover, such problems go undetected by the metrics often used to assess RDFs. To address these issues, we propose (I) a spectral Monte Carlo (SMC) method that yields g(r) as an analytical series expansion; and (II) a Sobolev norm that assesses the quality of RDFs by quantifying their fluctuations. Using the latter, we show that, relative to histogram-based approaches, SMC reduces by orders of magnitude both the noise in g(r) and the number of pair separations needed for acceptable convergence. Moreover, SMC reduces subjectivity and yields simple, differentiable formulas for the RDF, which are useful for tasks such as coarse-grained force-field calibration via iterative Boltzmann inversion." @default.
- W2952473833 created "2019-06-27" @default.
- W2952473833 creator A5033877939 @default.
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- W2952473833 date "2016-07-12" @default.
- W2952473833 modified "2023-09-27" @default.
- W2952473833 title "Beyond histograms: efficiently estimating radial distribution functions via spectral Monte Carlo" @default.
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- W2952473833 hasPublicationYear "2016" @default.
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