Data-driven construction of a generalized kinetic collision operator from molecular dynamics

• URL: http://arxiv.org/abs/2503.24208v2
• Date: Fri, 04 Apr 2025 21:43:31 GMT
• Title: Data-driven construction of a generalized kinetic collision operator from molecular dynamics
• Authors: Yue Zhao, Joshua W. Burby, Andrew Christlieb, Huan Lei,
• Abstract summary: We introduce a data-driven approach to learn a generalized kinetic collision operator from molecular dynamics.<n>Results show that preserving the broadly overlooked anisotropic nature of the collision energy transfer is crucial for predicting the plasma kinetics with non-negligible correlations.
• Score: 8.64881391784784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a data-driven approach to learn a generalized kinetic collision operator directly from molecular dynamics. Unlike the conventional (e.g., Landau) models, the present operator takes an anisotropic form that accounts for a second energy transfer arising from the collective interactions between the pair of collision particles and the environment. Numerical results show that preserving the broadly overlooked anisotropic nature of the collision energy transfer is crucial for predicting the plasma kinetics with non-negligible correlations, where the Landau model shows limitations.

Related papers

• Is memory all you need? Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows [38.33325744358047]
  We show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions.<n>This opens up a range of new applications, for example, for the control of active Lagrangian agents in turbulence.
  arXiv  Detail & Related papers  (2025-07-21T20:50:55Z)
• Neural Message Passing Induced by Energy-Constrained Diffusion [79.9193447649011]
  We propose an energy-constrained diffusion model as a principled interpretable framework for understanding the mechanism of MPNNs. We show that the new model can yield promising performance for cases where the data structures are observed (as a graph), partially observed or completely unobserved.
  arXiv  Detail & Related papers  (2024-09-13T17:54:41Z)
• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces novel deep dynamical models designed to represent continuous-time sequences.<n>We train the model using maximum likelihood estimation with Markov chain Monte Carlo.<n> Experimental results on oscillating systems, videos and real-world state sequences (MuJoCo) demonstrate that our model with the learnable energy-based prior outperforms existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks [36.136619420474766]
  We use a graph-convolutional neural network architecture to develop a highly automated training pipeline for coarse grained force fields. We show that this approach yields highly accurate force fields, but also that these force fields are more transferable through a variety of thermodynamic conditions.
  arXiv  Detail & Related papers  (2024-06-17T21:44:05Z)
• Quantized Thouless pumps protected by interactions in dimerized Rydberg tweezer arrays [41.94295877935867]
  In the noninteracting case, quantized Thouless pumps can only occur when a topological singularity is encircled adiabatically. In the presence of interactions, such topological transport can even persist for exotic paths in which the system gets arbitrarily close to the noninteracting singularity.
  arXiv  Detail & Related papers  (2024-02-14T16:58:21Z)
• Exploring exact-factorization-based trajectories for low-energy dynamics near a conical intersection [0.0]
  We study low-energy dynamics generated by a two-dimensional Jahn-Teller Hamiltonian in the vicinity of a conical intersection. We employ the exact factorization to understand how to model accurately low-energy dynamics in the vicinity of a conical intersection.
  arXiv  Detail & Related papers  (2024-01-26T11:46:38Z)
• Dynamical separation of charge and energy transport in one-dimensional Mott insulators [0.0]
  One-dimensional Mott insulators can be described using the sine-Gordon model. We demonstrate that this model exhibits separation of the transport of topological charge vs. energy.
  arXiv  Detail & Related papers  (2023-11-27T19:00:02Z)
• A Unified Interface Model for Dissipative Transport of Bosons and Fermions [0.0]
  We study the directed transport of bosons along a one dimensional lattice in a dissipative setting, where the hopping is only facilitated by coupling to a Markovian reservoir. By combining simulations with a field-theoretic analysis, we investigate the current fluctuations for this process and determine its behavior. These findings are relevant for experiments with cold atoms or long-lived quasi-particles in nanophotonic lattices, where such transport scenarios can be realized.
  arXiv  Detail & Related papers  (2023-11-16T19:00:01Z)
• NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields [65.07940731309856]
  Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids. In this paper, we consider a partially observable scenario known as fluid dynamics grounding. We propose a differentiable two-stage network named NeuroFluid. It is shown to reasonably estimate the underlying physics of fluids with different initial shapes, viscosity, and densities.
  arXiv  Detail & Related papers  (2022-03-03T15:13:29Z)
• A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization [0.1469945565246172]
  In this work, we suggest an end-to-end model which directly predicts per-atom energy from the coordinates of particles. We tested our model against several challenges in molecular simulation problems, including periodic boundary condition (PBC), $n$-body interaction, and binary composition.
  arXiv  Detail & Related papers  (2021-10-27T12:18:25Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• Analog cosmological reheating in an ultracold Bose gas [58.720142291102135]
  We quantum-simulate the reheating-like dynamics of a generic cosmological single-field model in an ultracold Bose gas. Expanding spacetime as well as the background oscillating inflaton field are mimicked in the non-relativistic limit. The proposed experiment has the potential of exploring the evolution up to late times even beyond the weak coupling regime.
  arXiv  Detail & Related papers  (2020-08-05T18:00:26Z)
• Dynamics of large deviations in the hydrodynamic limit: Non-interacting systems [0.0]
  We study the dynamics of the energy transferred across a point along a quantum chain. We consider the transverse field Ising and harmonic chains as prototypical models of non-interacting fermionic and bosonic excitations.
  arXiv  Detail & Related papers  (2020-07-23T16:33:58Z)
• Probing eigenstate thermalization in quantum simulators via fluctuation-dissipation relations [77.34726150561087]
  The eigenstate thermalization hypothesis (ETH) offers a universal mechanism for the approach to equilibrium of closed quantum many-body systems. Here, we propose a theory-independent route to probe the full ETH in quantum simulators by observing the emergence of fluctuation-dissipation relations. Our work presents a theory-independent way to characterize thermalization in quantum simulators and paves the way to quantum simulate condensed matter pump-probe experiments.
  arXiv  Detail & Related papers  (2020-07-20T18:00:02Z)

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.