Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths

• URL: http://arxiv.org/abs/2207.02149v2
• Date: Tue, 18 Jul 2023 10:09:13 GMT
• Title: Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths
• Authors: Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling
• Abstract summary: We consider the problem of sampling transition paths between two given metastable states of a molecular system. We propose a machine learning method for sampling said transitions.
• Score: 60.254555533113674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of sampling transition paths between two given metastable states of a molecular system, e.g. a folded and unfolded protein or products and reactants of a chemical reaction. Due to the existence of high energy barriers separating the states, these transition paths are unlikely to be sampled with standard Molecular Dynamics (MD) simulation. Traditional methods to augment MD with a bias potential to increase the probability of the transition rely on a dimensionality reduction step based on Collective Variables (CVs). Unfortunately, selecting appropriate CVs requires chemical intuition and traditional methods are therefore not always applicable to larger systems. Additionally, when incorrect CVs are used, the bias potential might not be minimal and bias the system along dimensions irrelevant to the transition. Showing a formal relation between the problem of sampling molecular transition paths, the Schr\"odinger bridge problem and stochastic optimal control with neural network policies, we propose a machine learning method for sampling said transitions. Unlike previous non-machine learning approaches our method, named PIPS, does not depend on CVs. We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins.

Related papers

• Detecting Quantum and Classical Phase Transitions via Unsupervised Machine Learning of the Fisher Information Metric [0.0]
  We develop an unsupervised machine learning (ML) method called ClassiFIM. We find that ClassiFIM reliably detects both topological (e.g., XXZ chain) and dynamical (e.g., metal-insulator transition in Hubbard model) quantum phase transitions.
  arXiv  Detail & Related papers  (2024-08-06T19:34:04Z)
• Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers [10.210248065533133]
  We introduce a novel approach that trains diffusion path samplers for transition path sampling. We recast the problem as an amortized sampling of the target path measure. We evaluate our approach, coined TPS-DPS, on a synthetic double-well potential and three peptides.
  arXiv  Detail & Related papers  (2024-05-30T11:32:42Z)
• 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)
• Telling different unravelings apart via nonlinear quantum-trajectory averages [0.272760415353533]
  The Gorini-Kossakowski-Sudarshan-Lindblad master equation governs the density matrix of open quantum systems. We propose a method to operationally distinguish unravelings produced by the same ME in different measurement scenarios. We show that a quantum-trajectory-averaged variance is able to distinguish these measurement scenarios.
  arXiv  Detail & Related papers  (2023-12-06T12:21:17Z)
• Diffusion Methods for Generating Transition Paths [6.222135766747873]
  In this work, we seek to simulate rare transitions between metastable states using score-based generative models. We develop two novel methods for path generation in this paper: a chain-based approach and a midpoint-based approach. Numerical results of generated transition paths for the M"uller potential and for Alanine dipeptide demonstrate the effectiveness of these approaches in both the data-rich and data-scarce regimes.
  arXiv  Detail & Related papers  (2023-09-19T03:03:03Z)
• 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)
• Local manifold learning and its link to domain-based physics knowledge [53.15471241298841]
  In many reacting flow systems, the thermo-chemical state-space is assumed to evolve close to a low-dimensional manifold (LDM) We show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space.
  arXiv  Detail & Related papers  (2022-07-01T09:06:25Z)
• Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions [65.74069050283998]
  We propose a computational framework to approximate Doob's $h$-transforms. The proposed approach can be orders of magnitude more efficient than state-of-the-art particle filters.
  arXiv  Detail & Related papers  (2022-06-07T15:03:05Z)
• GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation [102.85440102147267]
  We propose a novel generative model named GeoDiff for molecular conformation prediction. We show that GeoDiff is superior or comparable to existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-06T09:47:01Z)
• Generalized Discrete Truncated Wigner Approximation for Nonadiabtic Quantum-Classical Dynamics [0.0]
  We introduce a linearized semiclassical method, the generalized discrete truncated Wigner approximation (GDTWA) GDTWA samples the electron degrees of freedom in a discrete phase space, and forbids an unphysical growth of electronic state populations. Our results suggest that the method can be very adequate to treat challenging nonadiabatic dynamics problems in chemistry and related fields.
  arXiv  Detail & Related papers  (2021-04-14T21:53:35Z)

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.