Neural mode jump Monte Carlo
| Autor: | Frank Noé, Manuel Dibak, Luigi Sbailò |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010304 chemical physics
Artificial neural network Markov chain Computer science Monte Carlo method Complex system FOS: Physical sciences General Physics and Astronomy Markov chain Monte Carlo Computational Physics (physics.comp-ph) 010402 general chemistry 01 natural sciences 0104 chemical sciences symbols.namesake Biological Physics (physics.bio-ph) 0103 physical sciences Convergence (routing) symbols Jump Physics - Biological Physics Configuration space Statistical physics Physical and Theoretical Chemistry Physics - Computational Physics |
| Zdroj: | The Journal of Chemical Physics |
| ISSN: | 0021-9606 |
| DOI: | 10.1063/5.0032346 |
| Popis: | Markov chain Monte Carlo methods are a powerful tool for sampling equilibrium configurations in complex systems. One problem these methods often face is slow convergence over large energy barriers. In this work, we propose a novel method that increases convergence in systems composed of many metastable states. This method aims to connect metastable regions directly using generative neural networks in order to propose new configurations in the Markov chain and optimizes the acceptance probability of large jumps between modes in the configuration space. We provide a comprehensive theory as well as a training scheme for the network and demonstrate the method on example systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|