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Numerical study of the accuracy and efficiency of various approaches for Monte Carlo surface hopping calculations
- Source :
- The Journal of Chemical Physics. 122:094104
- Publication Year :
- 2005
- Publisher :
- AIP Publishing, 2005.
-
Abstract
- A one-dimensional, two-state model problem with two well-separated avoided crossing points is employed to test the efficiency and accuracy of a semiclassical surface hopping technique. The use of a one-dimensional model allows for the accurate numerical evaluation of both fully quantum-mechanical and semiclassical transition probabilities. The calculations demonstrate that the surface hopping procedure employed accounts for the interference between different hopping trajectories very well and provides highly accurate transition probabilities. It is, in general, not computationally feasible to completely sum over all hopping trajectories in the semiclassical calculations for multidimensional problems. In this case, a Monte Carlo procedure for selecting important trajectories can be employed. However, the cancellation due to the different phases associated with different trajectories limits the accuracy and efficiency of the Monte Carlo procedure. Various approaches for improving the accuracy and efficiency of Monte Carlo surface hopping procedures are investigated. These methods are found to significantly reduce the statistical sampling errors in the calculations, thereby increasing the accuracy of the transition probabilities obtained with a fixed number of trajectories sampled.
- Subjects :
- Hybrid Monte Carlo
Physics
Quantum Monte Carlo
Monte Carlo method
Dynamic Monte Carlo method
General Physics and Astronomy
Monte Carlo integration
Monte Carlo method in statistical physics
Quasi-Monte Carlo method
Statistical physics
Physical and Theoretical Chemistry
Monte Carlo molecular modeling
Subjects
Details
- ISSN :
- 10897690 and 00219606
- Volume :
- 122
- Database :
- OpenAIRE
- Journal :
- The Journal of Chemical Physics
- Accession number :
- edsair.doi.dedup.....e516d1ccada975716569148fb4a5f6c3