Configurational-Bias Algorithms

author

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).

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

M.G. Martin, and J.I. Siepmann, ‘Novel configurational-bias Monte Carlo method for branched molecules. Transferable potentials for phase equilibria. 2. United-atom description of branched alkanes’, J. Phys. Chem. B. 103, 4508-4517 (1999).

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

C.D. Wick, and J.I. Siepmann, ‘Self-adapting fixed-endpoint configurational-bias Monte Carlo method for the regrowth of interior segments of chain molecules with strong intramolecular interactions’, Macromolecules. 33, 7207-7218 (2000).

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

B. Chen, and J.I. Siepmann, ‘A novel Monte Carlo algorithm for simulating strongly associating fluids: Applications to water, hydrogen fluoride, and acetic acid’, J. Phys. Chem. B. 104, 8725-8734 (2000).
B. Chen, and J.I. Siepmann, ‘Improving the efficiency of the aggregation-volume-bias Monte Carlo algorithm’, J. Phys. Chem. B. 105, 11275-11282 (2001).

Allows for the efficient sampling of polarizable force fields

B. Chen, and J.I. Siepmann, ‘Monte Carlo algorithms for simulating systems with adiabatic separation of electronic and nuclear degrees of freedom’, Theor. Chem. Acc.. 103, 87-104 (1999).
B. Chen, J.J. Potoff, and J.I. Siepmann, ‘Adiabatic nuclear and electronic sampling Monte Carlo simulations in the Gibbs ensemble: Application to polarizable force fields for water’, J. Phys. Chem. B. 104, 2378-2390 (2000).

Allows for the exceedingly efficient sampling of nucleation phenomena

B. Chen, J.I. Siepmann, and M.L. Klein, ‘Simulating vapor-liquid nucleation of water: A combined histogram-reweighting and aggregation-volume-bias Monte Carlo investigation for fixed-charge and polarizable models’, J. Phys. Chem. A. 109, 1137-1145 (2005).