Enhanced Sampling of Configuration and Path Space in a Generalized
Ensemble by Shooting Point Exchange
- URL: http://arxiv.org/abs/2302.08757v2
- Date: Wed, 22 Mar 2023 09:54:23 GMT
- Title: Enhanced Sampling of Configuration and Path Space in a Generalized
Ensemble by Shooting Point Exchange
- Authors: Sebastian Falkner, Alessandro Coretti and Christoph Dellago
- Abstract summary: We propose a new approach to simulate rare events caused by transitions between long-lived states.
The scheme substantially enhances the efficiency of the transition path sampling simulations.
It yields information on thermodynamics, kinetics and reaction coordinates of molecular processes without distorting their dynamics.
- Score: 71.49868712710743
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The computer simulation of many molecular processes is complicated by long
time scales caused by rare transitions between long-lived states. Here, we
propose a new approach to simulate such rare events, which combines transition
path sampling with enhanced exploration of configuration space. The method
relies on exchange moves between configuration and trajectory space, carried
out based on a generalized ensemble. This scheme substantially enhances the
efficiency of the transition path sampling simulations, particularly for
systems with multiple transition channels, and yields information on
thermodynamics, kinetics and reaction coordinates of molecular processes
without distorting their dynamics. The method is illustrated using the
isomerization of proline in the KPTP tetrapeptide.
Related papers
- Transition Path Sampling with Boltzmann Generator-based MCMC Moves [49.69940954060636]
Current approaches to sample transition paths use Markov chain Monte Carlo and rely on time-intensive molecular dynamics simulations to find new paths.
Our approach operates in the latent space of a normalizing flow that maps from the molecule's Boltzmann distribution to a Gaussian, where we propose new paths without requiring molecular simulations.
arXiv Detail & Related papers (2023-12-08T20:05:33Z) - Conditioning Normalizing Flows for Rare Event Sampling [61.005334495264194]
We propose a transition path sampling scheme based on neural-network generated configurations.
We show that this approach enables the resolution of both the thermodynamics and kinetics of the transition region.
arXiv Detail & Related papers (2022-07-29T07:56:10Z) - Sampling Rare Conformational Transitions with a Quantum Computer [0.0]
We introduce a machine learning algorithm and MD simulations implemented on a classical computer with adiabatic quantum computing.
We derive a rigorous low-resolution representation of the system's dynamics, based on a small set of molecular configurations.
Our results provide a new paradigm for MD simulations to integrate machine learning and quantum computing.
arXiv Detail & Related papers (2022-01-27T19:46:06Z) - Traversing Time with Multi-Resolution Gaussian Process State-Space
Models [17.42262122708566]
We propose a novel Gaussian process state-space architecture composed of multiple components, each trained on a different resolution, to model effects on different timescales.
We benchmark our novel method on semi-synthetic data and on an engine modeling task.
In both experiments, our approach compares favorably against its state-of-the-art alternatives that operate on a single time-scale only.
arXiv Detail & Related papers (2021-12-06T18:39:27Z) - Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC [83.48593305367523]
Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sampling from complex continuous distributions.
We introduce a new approach based on augmenting Monte Carlo methods with SurVAE Flows to sample from discrete distributions.
We demonstrate the efficacy of our algorithm on a range of examples from statistics, computational physics and machine learning, and observe improvements compared to alternative algorithms.
arXiv Detail & Related papers (2021-02-04T02:21:08Z) - Complexity and Floquet dynamics: non-equilibrium Ising phase transitions [0.0]
We study the time-dependent circuit complexity of the periodically driven transverse field Ising model.
In the high-frequency driving limit the system is known to exhibit non-equilibrium phase transitions governed by the amplitude of the driving field.
arXiv Detail & Related papers (2020-08-31T19:13:03Z) - Universality of entanglement transitions from stroboscopic to continuous
measurements [68.8204255655161]
We show that the entanglement transition at finite coupling persists if the continuously measured system is randomly nonintegrable.
This provides a bridge between a wide range of experimental settings and the wealth of knowledge accumulated for the latter systems.
arXiv Detail & Related papers (2020-05-04T21:45:59Z) - Unsupervised machine learning of quantum phase transitions using
diffusion maps [77.34726150561087]
We show that the diffusion map method, which performs nonlinear dimensionality reduction and spectral clustering of the measurement data, has significant potential for learning complex phase transitions unsupervised.
This method works for measurements of local observables in a single basis and is thus readily applicable to many experimental quantum simulators.
arXiv Detail & Related papers (2020-03-16T18:40:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.