Algorithm Development

The configuration of an N-particle system in 3-dimensional space can be described classically by a point in a 3N dimensional configuration space. Only a few, very small parts of this 3N dimensional configuration space, termed "phase space", possess favorable (low) potential energies and make siginificant contributions to the average properties of the N-particle sytem. In contrast, the overwhelming part of configuration space is characterized by high potential energies and makes only a negligible contribution to the average properties. Sampling problems arise when the important regions of phase space are separated from each other by large free energy barriers. These large barriers cause sampling bottlenecks resulting in very long relaxation times. A prime challenge for particle-based simulations is to develop algorithms that allow the system to jump directly from one important region to another. This is usually achieved by special Monte Carlo algorithms that use specific biasing schemes to locate configurations that make siginificant contributions to the phase space averages. Over the past several years, the Siepmann group has contributed to the development of the following algorithms:

Configurational-Bias Monte Carlo (CBMC)

allows for the efficient sampling of the conformational space of linear chain molecules in condensed phases

  • J.I. Siepmann, 'A method for the direct calculation of chemical potentials for dense chain systems', Mol. Phys.. 70, 1145-1158 (1990).
  • J.I. Siepmann, and D. Frenkel, 'Configurational-bias Monte Carlo - A new sampling scheme for flexible chains', Mol. Phys.. 75, 59-70 (1992).

Coupled-Decoupled Configurational-Bias Monte Carlo (CD-CBMC)

allows for the efficient sampling of the conformational space of branched chain molecules

Self-Adapting Fixed-Endpoint Configurantional-Bias Monte Carlo (SAFE-CBMC)

allows for the efficient sampling of the conformational space of cyclic molecules and high-molecular-weight polymers

Aggregation-Volume-Bias Monte Carlo (AVBMC)

allows for the efficient sampling of the spatial distribution of aggregating (hydrogen-bonding) molecules

Adiabatic Nuclear Electronic Sampling Monte Carlo (ANES-MC)

allows for the efficient sampling of polarizable force fields

Aggregation-Volume-Bias Monte Carlo with Self-Adaptive Umbrella Sampling and Histogram Reweighting (AVUS-HR)

allows for the exceedingly efficient sampling of nucleation phenomena

Software Development

The Siepmann group contributes to the development of the following simulation programs that are distributed free of charge via GNU General Public License:

Monte Carlo for Complex Chemical Systems (MCCCS) Towhee

Car-Parrinello 2000 (CP2K)

The Siepmann group also contributes to Integrated Tools for Computational Chemical Dynamics software suite.

top of page

Chemistry Department Research News:

  • December 11, 2002: Simulating the Nucleation of Water/Ethanol and Water/Nonane Mixtures: Mutual Enhancement and Two-pathway Mechanism
  • June 23, 2004: Liquid Water from First Principles: Validation of Different Sampling Approaches
  • February 1, 2006: Simulating Fluid Phase Equilibria of Water from First Principles

Recent Algorithm Development Publications:

P. Bai, and J.I. Siepmann,
'Assessment and optimization of configurational-bias Monte Carlo particle swap strategies for simulations of water in the Gibbs ensemble,'
J. Chem. Theor. Comp., submitted for publication.

A.D. Cortes-Morales, I.G. Econonmou, C.J. Peters, and J.I. Siepmann,
'Influence of simulation protocols on the efficiency of Gibbs ensemble Monte Carlo simulations,'
Molec. Simul., 39, 1135-1142 (2013).

P. Bai, and J.I. Siepmann,
'Selective adsorption from dilute solutions: Gibbs ensemble Monte Carlo,'
Fluid Phase Equil., 351, 1-6 (2013).

S.L. Mielke, M. Dinpajooh, J.I. Siepmann, and D.G. Truhlar,
'Efficient methods for including quantum effects in Monte Carlo calculations on large systems: Extension of the displaced points path integral method and other effective potential methods to calculate properties and distributions,'
J. Chem. Phys., 138, 014110/15 pages (2013).

H.R. Leverentz, K.A. Maerzke, S.J. Keasler, J.I. Siepmann, and D.G. Truhlar,
'Electrostatically embedded many-body method for dipole moments, partial atomic charges, and charge transfer,'
Phys. Chem. Chem. Phys., 14, 7669-7678 (2012).

Chemical Theory Center | Chemistry Department | Chemical Engineering and Materials Science Department | Minnesota Supercomputing Institute | University of Minnesota | Internal