Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the Efficiency of Variationally Enhanced Sampling with
  Wavelet-Based Bias Potentials   Collective variable-based enhanced sampling methods are routinely used on
systems with metastable states, where high free energy barriers impede proper
sampling of the free energy landscapes when using conventional molecular
dynamics simulations. One such method is variationally enhanced sampling (VES),
which is based on a variational principle where a bias potential in the space
of some chosen slow degrees of freedom, or collective variables, is constructed
by minimizing a convex functional. In practice, the bias potential is taken as
a linear expansion in some basis function set. So far, primarily basis
functions delocalized in the collective variable space, like plane waves,
Chebyshev, or Legendre polynomials, have been used. However, there has not been
an extensive study of how the convergence behavior is affected by the choice of
the basis functions. In particular, it remains an open question if localized
basis functions might perform better. In this work, we implement, tune, and
validate Daubechies wavelets as basis functions for VES\@. The wavelets
construct orthogonal and localized bases that exhibit an attractive
multiresolution property. We evaluate the performance of wavelet and other
basis functions on various systems, going from model potentials to the calcium
carbonate association process in water. We observe that wavelets exhibit
excellent performance and much more robust convergence behavior than all other
basis functions, as well as better performance than metadynamics. In
particular, using wavelet bases yields far smaller fluctuations of the bias
potential within individual runs and smaller differences between independent
runs. Based on our overall results, we can recommend wavelets as basis
functions for VES.

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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: ao-sci