Neural Mode Jump Monte Carlo

Abstract

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 which 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 configuration space. We provide a comprehensive theory and demonstrate the method on example systems.

References in corpus (11)

• A high-bias, low-variance introduction to Machine Learning for physicists
• Neural Ordinary Differential Equations
• Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
• Deep neural network solution of the electronic Schrödinger equation
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
• Flow-based generative models for Markov chain Monte Carlo in lattice field theory
• Asymptotically unbiased estimation of physical observables with neural samplers
• Self-learning Monte Carlo with Deep Neural Networks
• Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
• Generalizing Hamiltonian Monte Carlo with Neural Networks
• Analysis and Optimization of Population Annealing