

Algorithm Development
The configuration of an Nparticle system in
3dimensional 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 Nparticle 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 particlebased 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:
ConfigurationalBias 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, 11451158
(1990).
 J.I. Siepmann, and D. Frenkel, 'Configurationalbias Monte Carlo  A new sampling scheme for
flexible chains', Mol. Phys.. 75, 5970
(1992).
CoupledDecoupled
ConfigurationalBias Monte Carlo (CDCBMC)
allows for the efficient sampling of the conformational space of branched
chain molecules
SelfAdapting FixedEndpoint
ConfigurantionalBias Monte Carlo (SAFECBMC)
allows for the efficient sampling of the conformational space of cyclic
molecules and highmolecularweight polymers
AggregationVolumeBias Monte
Carlo (AVBMC)
allows for the efficient sampling of the spatial distribution of aggregating
(hydrogenbonding) molecules
Adiabatic Nuclear
Electronic Sampling Monte Carlo (ANESMC)
allows for the efficient sampling of polarizable force fields
AggregationVolumeBias
Monte Carlo with SelfAdaptive Umbrella Sampling and Histogram Reweighting
(AVUSHR)
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
CarParrinello 2000 (CP2K) The Siepmann group also contributes to Integrated
Tools for Computational Chemical Dynamics software suite.
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Chemistry Department Research
News:
 December
11, 2002: Simulating the Nucleation of Water/Ethanol and Water/Nonane
Mixtures: Mutual Enhancement and Twopathway 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:

T.P. LiyanaArarchchi, S.N. Jamadagni, D. Eike, P.H. Koenig, and J.I. Siepmann,
 'Liquidliquid equilibria for softrepulsive particles: Improved
equation of state and methodology for representing molecules of
different sizes and chemistry in dissipative particle dynamics,'
 J. Chem. Phys., 142, art. no. 044902/13 pages (2015).

A.D. CortesMorales, 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, 11351142 (2013).

P. Bai, and J.I. Siepmann,
 'Selective adsorption from dilute solutions: Gibbs ensemble Monte
Carlo simulations,'
 Fluid Phase Equil., 351, 16 (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, art. no. 014110/15 pages (2013).

H.R. Leverentz, K.A. Maerzke, S.J. Keasler, J.I. Siepmann, and D.G. Truhlar,
 'Electrostatically embedded manybody method for dipole moments,
partial atomic charges, and charge transfer,'
 Phys. Chem. Chem. Phys., 14, 76697678 (2012).
