Theoretical Chemistry Accounts

, 135:215 | Cite as

Accelerating the computation of bath spectral densities with super-resolution

  • Thomas Markovich
  • Samuel M. Blau
  • John Parkhill
  • Christoph Kreisbeck
  • Jacob N. Sanders
  • Xavier Andrade
  • Alán Aspuru-GuzikEmail author
Regular Article
Part of the following topical collections:
  1. Festschrift in honour of A. Vela


Quantum transport and other phenomena are typically modeled by coupling the system of interest to an environment, or bath, held at thermal equilibrium. Realistic bath models are at least as challenging to construct as models for the quantum systems themselves, since they must incorporate many degrees of freedom that interact with the system on a wide range of timescales. Owing to computational limitations, the environment is often modeled with simple functional forms, with a few parameters fit to experiment to yield semi-quantitative results. Growing computational resources have enabled the construction of more realistic bath models from molecular dynamics (MD) simulations. In this paper, we develop a numerical technique to construct these atomistic bath models with better accuracy and decreased cost. We apply a novel signal processing technique, known as super-resolution, combined with a dictionary of physically motivated bath modes to derive spectral densities from MD simulations. Our approach reduces the required simulation time and provides a more accurate spectral density than can be obtained via standard Fourier transform methods. Moreover, the spectral density is provided as a convenient closed-form expression which yields an analytic time-dependent bath kernel. Exciton dynamics of the Fenna–Matthews–Olson light-harvesting complex are simulated with a second-order time-convolutionless master equation, and spectral densities constructed via super-resolution are shown to reproduce the dynamics using only a quarter of the amount of MD data.


Spectral densities Super-resolution Hierarchical equations of motion 



We acknowledge S. Valleau for useful discussions and computer code. We acknowledge the financial support of Defense Advanced Research Projects Agency Grant N66001-10-1-4063 and the Defense Threat Reduction Agency under Contract No. HDTRA1-10-1-0046. T.M. acknowledges support from the National Science Foundation (NSF) through the Graduate Research Fellowship Program (GRFP). S.B. acknowledges support from the Department of Energy (DoE) through the Computational Sciences Graduate Fellowship (CSGF). J.N.S. acknowledges support from the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. A.A.G. thanks the Corning Foundation.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Thomas Markovich
    • 1
  • Samuel M. Blau
    • 1
  • John Parkhill
    • 2
  • Christoph Kreisbeck
    • 1
  • Jacob N. Sanders
    • 1
  • Xavier Andrade
    • 1
  • Alán Aspuru-Guzik
    • 1
    Email author
  1. 1.Department of Chemistry and Chemical BiologyHarvard UniversityCambridgeUSA
  2. 2.Department of ChemistryThe University of Notre DameSouth BendUSA

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