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
Configurational-Bias Monte Carlo
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
- J.I. Siepmann, and D. Frenkel, 'Configurational-bias Monte Carlo - A new sampling scheme for
flexible chains', Mol. Phys.. 75, 59-70
Configurational-Bias Monte Carlo (CD-CBMC)
allows for the efficient sampling of the conformational space of branched
Configurantional-Bias Monte Carlo (SAFE-CBMC)
allows for the efficient sampling of the conformational space of cyclic
molecules and high-molecular-weight polymers
allows for the efficient sampling of the spatial distribution of aggregating
Electronic Sampling Monte Carlo (ANES-MC)
allows for the efficient sampling of polarizable force fields
Monte Carlo with Self-Adaptive Umbrella Sampling and Histogram Reweighting
allows for the exceedingly efficient sampling of nucleation phenomena
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.
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Chemistry Department Research
11, 2002: Simulating the Nucleation of Water/Ethanol and Water/Nonane
Mixtures: Mutual Enhancement and Two-pathway Mechanism
23, 2004: Liquid Water from First Principles: Validation of Different
- February 1, 2006: Simulating Fluid Phase Equilibria of Water from
P. Bai, and J.I. Siepmann,
- 'Assessment and optimization of configurational-bias Monte Carlo
particle swap strategies for simulations of water in the Gibbs
- 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).