Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules
We describe a new method to calculate the vibrational ground state properties of weakly bound molecular systems and apply it to (HF)2 and HF-HC1. A Bayesian Inference neural network is used to fit an analytic function to a set of ab initia data points, which may then be employed by the quantum diffu...
Autori principali: | Brown, D, Gibbs, M, Clary, D |
---|---|
Natura: | Journal article |
Pubblicazione: |
1996
|
Documenti analoghi
Documenti analoghi
-
Speed improvement of diffusion quantum Monte Carlo calculations on weakly bound clusters
di: Benoit, D, et al.
Pubblicazione: (1998) -
Ab initio and diffusion Monte Carlo study of uracil-water, thymine-water, cytosine-water, and cytosine-(water)(2)
di: van Mourik, T, et al.
Pubblicazione: (2000) -
QUANTUM SIMULATION OF WEAKLY-BOUND COMPLEXES USING DIRECT AB-INITIO ENERGY POINTS
di: Gregory, J, et al.
Pubblicazione: (1995) -
Monte Carlo methods in Ab Initio quantum chemistry /
di: 307648 Hammond, B. L., et al.
Pubblicazione: (1994) -
Ab-initio molecular dynamics with quantum Monte Carlo
di: Ye eLuo, et al.
Pubblicazione: (2015-04-01)