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...
| Main Authors: | , , |
|---|---|
| 格式: | Journal article |
| 出版: |
1996
|
| _version_ | 1826301927253606400 |
|---|---|
| author | Brown, D Gibbs, M Clary, D |
| author_facet | Brown, D Gibbs, M Clary, D |
| author_sort | Brown, D |
| collection | OXFORD |
| description | 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 diffusion Monte Carlo method to produce ground state vibrational wave functions and properties. The method is general and relatively simple to implement and will be attractive for calculations on systems for which no analytic potential energy surface exists. © 1996 American Institute of Physics. |
| first_indexed | 2024-03-07T05:39:46Z |
| format | Journal article |
| id | oxford-uuid:e52ac821-743f-4e49-a1e1-084f6fe23b74 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:39:46Z |
| publishDate | 1996 |
| record_format | dspace |
| spelling | oxford-uuid:e52ac821-743f-4e49-a1e1-084f6fe23b742022-03-27T10:22:00ZCombining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound moleculesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e52ac821-743f-4e49-a1e1-084f6fe23b74Symplectic Elements at Oxford1996Brown, DGibbs, MClary, DWe 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 diffusion Monte Carlo method to produce ground state vibrational wave functions and properties. The method is general and relatively simple to implement and will be attractive for calculations on systems for which no analytic potential energy surface exists. © 1996 American Institute of Physics. |
| spellingShingle | Brown, D Gibbs, M Clary, D Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules |
| title | Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules |
| title_full | Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules |
| title_fullStr | Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules |
| title_full_unstemmed | Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules |
| title_short | Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules |
| title_sort | combining ab initio computations neural networks and diffusion monte carlo an efficient method to treat weakly bound molecules |
| work_keys_str_mv | AT brownd combiningabinitiocomputationsneuralnetworksanddiffusionmontecarloanefficientmethodtotreatweaklyboundmolecules AT gibbsm combiningabinitiocomputationsneuralnetworksanddiffusionmontecarloanefficientmethodtotreatweaklyboundmolecules AT claryd combiningabinitiocomputationsneuralnetworksanddiffusionmontecarloanefficientmethodtotreatweaklyboundmolecules |