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  • Open Access

Effective classical potential for quantum statistical averages

Vijay Ganesh Sadhasivam1, Stuart C. Althorpe1, and Venkat Kapil1,2,3,*

  • *Contact author: v.kapil@ucl.ac.uk

Phys. Rev. E 114, 024126 – Published 14 August, 2026

DOI: https://doi.org/10.1103/zr37-gtc5

Abstract

We present an effective potential that allows quantum thermal expectation values of a position-dependent observable to be estimated as a classical ensemble average of the corresponding function. We follow the approach of Feynman and Hibbs but perform the mean-field treatment of quantum fluctuations about the path starting point rather than the path centroid. Furthermore, rather than performing a full variational optimization of the potential, we explore approximate functional forms that yield numerical robustness. The resulting closed-form potential is exact in the classical and harmonic limits; benchmarks against exact position distributions for one-dimensional quartic, Morse, and double-well potentials show good agreement for potentials with harmonic support.

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References (57)

  1. R. P. Feynman and A. R. Hibbs, Quantum Mechanics and Path Integrals, International Series in Pure and Applied Physics (McGraw-Hill, New York, NY, 1965).
  2. J. A. Barker, A quantum-statistical Monte Carlo method; path integrals with boundary conditions, J. Chem. Phys. 70, 2914 (1979).
  3. D. Chandler and P. G. Wolynes, Exploiting the isomorphism between quantum theory and classical statistical mechanics of polyatomic fluids, J. Chem. Phys. 74, 4078 (1981).
  4. M. Parrinello and A. Rahman, Study of an F center in molten KCl, J. Chem. Phys. 80, 860 (1984).
  5. T. E. Markland and D. E. Manolopoulos, A refined ring polymer contraction scheme for systems with electrostatic interactions, Chem. Phys. Lett. 464, 256 (2008).
  6. V. Kapil, J. Wieme, S. Vandenbrande, A. Lamaire, V. Van Speybroeck, and M. Ceriotti, Modeling the structural and thermal properties of loaded metal–organic frameworks. An interplay of quantum and anharmonic fluctuations, J. Chem. Theory Comput. 15, 3237 (2019).
  7. M. Suzuki, Hybrid exponential product formulas for unbounded operators with possible applications to Monte Carlo simulations, Phys. Lett. A 201, 425 (1995).
  8. S. Jang, S. Jang, and G. A. Voth, Applications of higher order composite factorization schemes in imaginary time path integral simulations, J. Chem. Phys. 115, 7832 (2001).
  9. A. Pérez and M. E. Tuckerman, Improving the convergence of closed and open path integral molecular dynamics via higher order Trotter factorization schemes, J. Chem. Phys. 135, 064104 (2011).
  10. V. Kapil, J. Behler, and M. Ceriotti, High order path integrals made easy, J. Chem. Phys. 145, 234103 (2016).
  11. M. Takahashi and M. Imada, Monte Carlo calculation of quantum systems. II. Higher order correction, J. Phys. Soc. Jpn. 53, 3765 (1984).
  12. I. Poltavsky and A. Tkatchenko, Modeling quantum nuclei with perturbed path integral molecular dynamics, Chem. Sci. 7, 1368 (2016).
  13. I. Poltavsky, V. Kapil, M. Ceriotti, K. S. Kim, and A. Tkatchenko, Accurate description of nuclear quantum effects with high-order perturbed path integrals (HOPPI), J. Chem. Theory Comput. 16, 1128 (2020).
  14. T. E. Markland and D. E. Manolopoulos, An efficient ring polymer contraction scheme for imaginary time path integral simulations, J. Chem. Phys. 129, 024105 (2008).
  15. V. Kapil, J. VandeVondele, and M. Ceriotti, Accurate molecular dynamics and nuclear quantum effects at low cost by multiple steps in real and imaginary time: Using density functional theory to accelerate wavefunction methods, J. Chem. Phys. 144, 054111 (2016).
  16. O. Marsalek and T. E. Markland, Quantum dynamics and spectroscopy of ab initio liquid water: The interplay of nuclear and electronic quantum effects, J. Phys. Chem. Lett. 8, 1545 (2017).
  17. M. Ceriotti, G. Bussi, and M. Parrinello, Nuclear quantum effects in solids using a colored-noise thermostat, Phys. Rev. Lett. 103, 030603 (2009).
  18. H. Dammak, Y. Chalopin, M. Laroche, M. Hayoun, and J.-J. Greffet, Quantum thermal bath for molecular dynamics simulation, Phys. Rev. Lett. 103, 190601 (2009).
  19. M. Ceriotti, D. E. Manolopoulos, and M. Parrinello, Accelerating the convergence of path integral dynamics with a generalized Langevin equation, J. Chem. Phys. 134, 084104 (2011).
  20. T. E. Markland and M. Ceriotti, Nuclear quantum effects enter the mainstream, Nat. Rev. Chem. 2, 0109 (2018).
  21. E. Wigner, On the quantum correction for thermodynamic equilibrium, Phys. Rev. 40, 749 (1932).
  22. J. G. Kirkwood, Quantum statistics of almost classical assemblies, Phys. Rev. 44, 31 (1933).
  23. J. Zinn-Justin, Path Integrals in Quantum Mechanics (Oxford University Press, Oxford, 2010).
  24. G. Wentzel, Eine Verallgemeinerung der Quantenbedingungen für die Zwecke der Wellenmechanik, Z. Phys. 38, 518 (1926).
  25. H. A. Kramers, Wellenmechanik und halbzahlige Quantisierung, Z. Phys. 39, 828 (1926).
  26. L. Brillouin, Remarques sur la mécanique ondulatoire, J. Phys. Radium 7, 353 (1926).
  27. R. P. Feynman and H. Kleinert, Effective classical partition functions, Phys. Rev. A 34, 5080 (1986).
  28. R. Giachetti and V. Tognetti, Variational approach to quantum statistical mechanics of nonlinear systems with application to sine-Gordon chains, Phys. Rev. Lett. 55, 912 (1985).
  29. A. Cuccoli, R. Giachetti, V. Tognetti, R. Vaia, and P. Verrucchi, The effective potential and effective Hamiltonian in quantum statistical mechanics, J. Phys.: Condens. Matter 7, 7891 (1995).
  30. B. Guillot and Y. Guissani, Quantum effects in simulated water by the Feynman–Hibbs approach, J. Chem. Phys. 108, 10162 (1998).
  31. J. Cao and G. A. Voth, A new perspective on quantum time correlation functions, J. Chem. Phys. 99, 10070 (1993).
  32. G. Trenins and S. C. Althorpe, Mean-field Matsubara dynamics: Analysis of path-integral curvature effects in rovibrational spectra, J. Chem. Phys. 149, 014102 (2018).
  33. G. Trenins, M. J. Willatt, and S. C. Althorpe, Path-integral dynamics of water using curvilinear centroids, J. Chem. Phys. 151, 054109 (2019).
  34. C. Haggard, V. G. Sadhasivam, G. Trenins, and S. C. Althorpe, Testing the quasicentroid molecular dynamics method on gas-phase ammonia, J. Chem. Phys. 155, 174120 (2021).
  35. H. Kleinert, Particle distribution from effective classical potential, Phys. Lett. A 118, 267 (1986).
  36. J. Cao and G. A. Voth, The formulation of quantum statistical mechanics based on the Feynman path centroid density. II. Dynamical properties, J. Chem. Phys. 100, 5106 (1994).
  37. A. Royer, Cumulant approximations and renormalized Wigner-Kirkwood expansion for quantum Boltzmann densities, Phys. Rev. A 32, 1729 (1985).
  38. A. Royer, Partial resummation of the Wigner–Kirkwood expansion for quantum Boltzmann densities, Chem. Phys. Lett. 116, 142 (1985).
  39. W. H. Miller, Classical path approximation for the Boltzmann density matrix, J. Chem. Phys. 55, 3146 (1971).
  40. M. Ovchinnikov, V. Apkarian, and G. A. Voth, Semiclassical molecular dynamics computation of spontaneous light emission in the condensed phase: Resonance Raman spectra, J. Chem. Phys. 114, 7130 (2001).
  41. Q. Shi and E. Geva, Semiclassical theory of vibrational energy relaxation in the condensed phase, J. Phys. Chem. A 107, 9059 (2003).
  42. J. Liu and W. H. Miller, A simple model for the treatment of imaginary frequencies in chemical reaction rates and molecular liquids, J. Chem. Phys. 131, 074113 (2009).
  43. A. V. Sinitskiy and G. A. Voth, A reductionist perspective on quantum statistical mechanics: Coarse-graining of path integrals, J. Chem. Phys. 143, 094104 (2015).
  44. W. H. Ryu, Y. Han, and G. A. Voth, Coarse-graining of many-body path integrals: Theory and numerical approximations, J. Chem. Phys. 150, 244103 (2019).
  45. W. H. Ryu and G. A. Voth, Coarse-graining of imaginary time Feynman path integrals: Inclusion of intramolecular interactions and bottom-up force-matching, J. Phys. Chem. A 126, 6004 (2022).
  46. D. T. Colbert and W. H. Miller, A novel discrete variable representation for quantum mechanical reactive scattering via the S -matrix Kohn method, J. Chem. Phys. 96, 1982 (1992).
  47. See Supplemental Material at http://link.aps.org/supplemental/10.1103/zr37-gtc5 for a comparison of the renormalized and unrenormalized local harmonic effective potentials for the quartic and Morse oscillators, a comparison of the resulting position distributions with those from the Feynman–Hibbs and Feynman–Kleinert centroid effective potentials, and a demonstration of the validity of the local harmonic approximation at absolute zero.
  48. M. Rossi, M. Ceriotti, and D. E. Manolopoulos, How to remove the spurious resonances from ring polymer molecular dynamics, J. Chem. Phys. 140, 234116 (2014).
  49. N. R. Werthamer, Self-consistent phonon formulation of anharmonic lattice dynamics, Phys. Rev. B 1, 572 (1970).
  50. F. Musil, A. Grisafi, A. P. Bartók, C. Ortner, G. Csányi, and M. Ceriotti, Physics-inspired structural representations for molecules and materials, Chem. Rev. 121, 9759 (2021).
  51. T. D. Loose, P. G. Sahrmann, and G. A. Voth, Centroid molecular dynamics can be greatly accelerated through neural network learned centroid forces derived from path integral molecular dynamics, J. Chem. Theory Comput. 18, 5856 (2022).
  52. I. Zaporozhets, F. Musil, V. Kapil, and C. Clementi, Accurate nuclear quantum statistics on machine-learned classical effective potentials, J. Chem. Phys. 161, 134102 (2024).
  53. J. Behler and M. Parrinello, Generalized neural-network representation of high-dimensional potential-energy surfaces, Phys. Rev. Lett. 98, 146401 (2007).
  54. A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons, Phys. Rev. Lett. 104, 136403 (2010).
  55. A. Grisafi and M. Ceriotti, Incorporating long-range physics in atomic-scale machine learning, J. Chem. Phys. 151, 204105 (2019).
  56. V. Sadhasivam, V. Kapil, and S. Althorpe, Research data supporting “Effective Classical Potential for Quantum Statistical Averages”, Apollo - University of Cambridge Repository, 2026, https://doi.org/10.17863/CAM.129713.
  57. L. Barragán-Gil and R. Walser, Harmonic oscillator thermal density matrix: First-order differential equations for the position representation, Am. J. Phys. 86, 22 (2018).

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