Path integral molecular dynamics simulations for Green's function in a system of identical bosons

• URL: http://arxiv.org/abs/2203.09919v1
• Date: Thu, 17 Mar 2022 01:06:07 GMT
• Title: Path integral molecular dynamics simulations for Green's function in a system of identical bosons
• Authors: Xiong Yunuo, Xiong Hongwei
• Abstract summary: We extend PIMD techniques to study Green's function for bosonic systems. We also apply our method to systems of identical interacting bosons to study Berezinskii-Kosterlitz-Thouless transition around its critical temperature.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Path integral molecular dynamics (PIMD) has been successfully applied to perform simulations of large bosonic systems in a recent work (Hirshberg et al., PNAS, 116, 21445 (2019)). In this work we extend PIMD techniques to study Green's function for bosonic systems. We demonstrate that the development of the original PIMD method enables us to calculate Green's function and extract momentum distribution from our simulations. We also apply our method to systems of identical interacting bosons to study Berezinskii-Kosterlitz-Thouless transition around its critical temperature.

Related papers

• Variational Green's Functions for Volumetric PDEs [5.958368061748122]
  We present a method that learns a smooth, differentiable representation of the Green's function for linear self-adjoint PDE operators.<n>To resolve the sharp singularities characteristic of the Green's functions, our method decomposes the Green's function into an analytic free-space component, and a learned corrector component.<n>The resulting Green's functions are fast to evaluate, differentiable with respect to source application, and can be conditioned on other signals parameterizing our geometry.
  arXiv  Detail & Related papers  (2026-02-12T19:12:44Z)
• Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design [53.93023688824764]
  We address the problem of fine-tuning diffusion models for reward-guided generation in biomolecular design.<n>We propose an iterative distillation-based fine-tuning framework that enables diffusion models to optimize for arbitrary reward functions.<n>Our off-policy formulation, combined with KL divergence minimization, enhances training stability and sample efficiency compared to existing RL-based methods.
  arXiv  Detail & Related papers  (2025-07-01T05:55:28Z)
• Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models [50.77646970127369]
  We propose an energy-based diffusion model with a Fokker--Planck-derived regularization term to enforce consistency.<n>We demonstrate our approach by sampling and simulating multiple biomolecular systems, including fast-folding proteins.
  arXiv  Detail & Related papers  (2025-06-20T16:38:29Z)
• An explainable operator approximation framework under the guideline of Green's function [1.1174586184779578]
  We introduce a novel framework, GreensONet, to learn embedded Green's functions and solve PDEs via Green's integral formulation. The framework's accuracy and generalization ability surpass those of existing methods.
  arXiv  Detail & Related papers  (2024-12-21T14:31:03Z)
• Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space [72.52365911990935]
  We introduce Bellman Diffusion, a novel DGM framework that maintains linearity in MDPs through gradient and scalar field modeling. Our results show that Bellman Diffusion achieves accurate field estimations and is a capable image generator, converging 1.5x faster than the traditional histogram-based baseline in distributional RL tasks.
  arXiv  Detail & Related papers  (2024-10-02T17:53:23Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Stochastic Schr\"odinger equation approach to real-time dynamics of Anderson-Holstein impurities: an open quantum system perspective [3.105656247358225]
  We develop a framework to simulate dynamics of Anderson-Holstein impurities coupled to a continuous fermionic bath. We show that such an SSE treatment provides a middle ground between numerically expansive microscopic simulations and master equations.
  arXiv  Detail & Related papers  (2023-09-16T06:03:54Z)
• Computing the many-body Green's function with adaptive variational quantum dynamics [0.0]
  We present a method to compute the many-body real-time Green's function using an adaptive variational quantum dynamics simulation approach. We demonstrate the evaluation of Green's function on an IBM Q quantum computer.
  arXiv  Detail & Related papers  (2023-02-06T19:49:16Z)
• Agnostic Physics-Driven Deep Learning [82.89993762912795]
  This work establishes that a physical system can perform statistical gradient learning without gradient computations. In Aeqprop, the specifics of the system do not have to be known: the procedure is based on external manipulations. Aeqprop also establishes that in natural (bio)physical systems, genuine gradient-based statistical learning may result from generic, relatively simple mechanisms.
  arXiv  Detail & Related papers  (2022-05-30T12:02:53Z)
• Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data [0.6882042556551611]
  We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs) The fine scale, detailed, hybrid (continuum - Monte Carlo) simulation model embodies the underlying biophysics. We learn effective, coarse-grained "Keller-Segel class" chemotactic PDEs using machine learning regressors.
  arXiv  Detail & Related papers  (2022-05-26T03:02:49Z)
• Numerical calculation of Green's function and momentum distribution for spin-polarized fermions by path integral molecular dynamics [0.0]
  PIMD can be developed to calculate Green's function and extract momentum distribution for spin-polarized fermions. We show that PIMD has potential application to numerous quantum systems, such as cold atom simulation of Mott insulator in Fermi-Hubbard model.
  arXiv  Detail & Related papers  (2022-04-05T11:09:09Z)
• Exact formulas of the end-to-end Green's functions in non-Hermitian systems [0.0]
  Green's function in non-Hermitian systems can be capable of directional amplification. We derive exact formulas for the end-to-end Green's functions of single-band systems.
  arXiv  Detail & Related papers  (2021-09-07T12:36:42Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Method of spectral Green functions in driven open quantum dynamics [77.34726150561087]
  A novel method based on spectral Green functions is presented for the simulation of driven open quantum dynamics. The formalism shows remarkable analogies to the use of Green functions in quantum field theory. The method dramatically reduces computational cost compared with simulations based on solving the full master equation.
  arXiv  Detail & Related papers  (2020-06-04T09:41:08Z)
• A Near-Optimal Gradient Flow for Learning Neural Energy-Based Models [93.24030378630175]
  We propose a novel numerical scheme to optimize the gradient flows for learning energy-based models (EBMs) We derive a second-order Wasserstein gradient flow of the global relative entropy from Fokker-Planck equation. Compared with existing schemes, Wasserstein gradient flow is a smoother and near-optimal numerical scheme to approximate real data densities.
  arXiv  Detail & Related papers  (2019-10-31T02:26:20Z)

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.