By David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar, Ilya Prigogine, Stuart A. Rice
In Monte Carlo equipment in Chemical Physics: An advent to the Monte Carlo technique for Particle Simulations J. Ilja Siepmann Random quantity turbines for Parallel purposes Ashok Srinivasan, David M. Ceperley and Michael Mascagni among Classical and Quantum Monte Carlo tools: "Variational" QMC Dario Bressanini and Peter J. Reynolds Monte Carlo Eigenvalue equipment in Quantum Mechanics and Statistical Mechanics M. P. Nightingale and C.J. Umrigar Adaptive Path-Integral Monte Carlo equipment for actual Computation of Molecular Thermodynamic homes Robert Q. Topper Monte Carlo Sampling for Classical Trajectory Simulations Gilles H. Peslherbe Haobin Wang and William L. Hase Monte Carlo methods to the Protein Folding challenge Jeffrey Skolnick and Andrzej Kolinski Entropy Sampling Monte Carlo for Polypeptides and Proteins Harold A. Scheraga and Minh-Hong Hao Macrostate Dissection of Thermodynamic Monte Carlo Integrals Bruce W. Church, Alex Ulitsky, and David Shalloway Simulated Annealing-Optimal Histogram tools David M. Ferguson and David G. Garrett Monte Carlo equipment for Polymeric structures Juan J. de Pablo and Fernando A. Escobedo Thermodynamic-Scaling equipment in Monte Carlo and Their software to part Equilibria John Valleau Semigrand Canonical Monte Carlo Simulation: Integration alongside Coexistence traces David A. Kofke Monte Carlo equipment for Simulating section Equilibria of advanced Fluids J. Ilja Siepmann Reactive Canonical Monte Carlo J. Karl Johnson New Monte Carlo Algorithms for Classical Spin platforms G. T. Barkema and M.E.J. NewmanContent:
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Extra info for Advances in Chemical Physics: Monte Carlo Methods in Chemical Physics, Volume 105
The position vector R which we use, lives in the 3M-dimensional coordinate space of the M (quantum) particles comprising the system. This vector is, for example, the argument of the trial wavefunction YT(R); however, sometimes we will omit the explicit dependence on R to avoid cluttering the equations, and simply write YT. , the exponent of a Gaussian or Slater orbital), we may write YT(R;a), or simply YT(a),again omitting the explicit R dependence. 1’ Once such a distribution is established, expectation values of various quantities may be sampled.
This omission results in generally slow convergence of the resultant series. An important characteristic of Monte Carlo methods is their ability to use arbitrary wavefunction forms, including ones having explicit interelectronic distance and other many-body dependencies. This enables greater flexibility and hence more compact representation than is possible with forms constructed solely with one-electron functions. The one-electron form, however, provides a useful starting point for constructing the more general forms we desire.
Optimization Using Derivatives D. Optimization of the Variance of the Local Energy E. Optimization with a Fixed Ensemble V. Acceleration Techniques VI. Recent Results and New Directions VII. Conclusions I. INTRODUCTION The variational Monte Carlo method is reviewed here. It is in essence a * Also Department of Physics, Georgetown University, Washington, DC (USA). Advances in Chemical Physics, Volume 105, Monte Carlo Methods in Chemical Physics, edited by David M. Ferguson, J. Ilja Siepmann, and Donald G.
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