Cover Image

Boltzmann Generators for Condensed Matter via Riemannian Flow Matching

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hoffmann, Emil, Schebek, Maximilian, Klein, Leon, Noé, Frank, Rogal, Jutta
Format:	Preprint
Published:	2026
Subjects:	Computational Physics Statistical Mechanics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Scalable Boltzmann Generators for equilibrium sampling of large-scale materials
by: Schebek, Maximilian, et al.
Published: (2025)

Efficient mapping of phase diagrams with conditional Boltzmann Generators
by: Schebek, Maximilian, et al.
Published: (2024)

Assessing generative modeling approaches for free energy estimates in condensed matter
by: Schebek, Maximilian, et al.
Published: (2025)

Estimating Solvation Free Energies with Boltzmann Generators
by: Schebek, Maximilian, et al.
Published: (2025)

Riemannian Stochastic Interpolants for Amorphous Particle Systems
by: Grenioux, Louis, et al.
Published: (2025)

Transferable Boltzmann Generators
by: Klein, Leon, et al.
Published: (2024)

Coarse-Grained Boltzmann Generators
by: Chen, Weilong, et al.
Published: (2026)

Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
by: Kim, Gilhan, et al.
Published: (2026)

A phase transition in sampling from Restricted Boltzmann Machines
by: Kwon, Youngwoo, et al.
Published: (2024)

AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
by: Kanaujia, Vikas, et al.
Published: (2024)

Efficient Identification of Critical Transitions via Flow Matching: A Scalable Generative Approach for Many-Body Systems
by: Lee, Qian-Rui, et al.
Published: (2025)

Fundamentals of quantum Boltzmann machine learning with visible and hidden units
by: Wilde, Mark M.
Published: (2025)

Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks
by: Lanzoni, Daniele, et al.
Published: (2023)

Meta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines
by: Bhat, Ruchira V, et al.
Published: (2025)

Implicit Binarization via Complex Phase Dynamics in Combinatorial Optimization
by: Cohen, Khen, et al.
Published: (2026)

Quantum statistics from classical simulations via generative Gibbs sampling
by: Wang, Weizhou, et al.
Published: (2026)

Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning
by: Man, Hai, et al.
Published: (2026)

Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models
by: Li, Shuo-Hui, et al.
Published: (2026)

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations
by: Christiansen, Henrik, et al.
Published: (2025)

Controlling dynamics of stochastic systems with deep reinforcement learning
by: Mukhamadiarov, Ruslan
Published: (2025)

Model selection for stochastic dynamics: a parsimonious and principled approach
by: Gerardos, Andonis
Published: (2025)

Quantum Boltzmann machine learning of ground-state energies
by: Patel, Dhrumil, et al.
Published: (2024)

Natural gradient and parameter estimation for quantum Boltzmann machines
by: Patel, Dhrumil, et al.
Published: (2024)

Learning and Generating Mixed States Prepared by Shallow Channel Circuits
by: Hu, Fangjun, et al.
Published: (2026)

Latent Thermodynamic Flows: Unified Representation Learning and Generative Modeling of Temperature-Dependent Behaviors from Limited Data
by: Qiu, Yunrui, et al.
Published: (2025)

Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles
by: McCandler, Caitlin A., et al.
Published: (2026)

Regularized Fluctuating Lattice Boltzmann Model
by: Lauricella, Marco, et al.
Published: (2025)

Learning conformational ensembles of proteins based on backbone geometry
by: Wolf, Nicolas, et al.
Published: (2025)

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
by: Schaaf, Lars L., et al.
Published: (2024)

Generative Learning of Continuous Data by Tensor Networks
by: Meiburg, Alex, et al.
Published: (2023)

CrystalGRW: Generative Modeling of Crystal Structures with Targeted Properties via Geodesic Random Walks
by: Tangsongcharoen, Krit, et al.
Published: (2025)

An exact multiple-time-step variational formulation for the committor and the transition rate
by: Lorpaiboon, Chatipat, et al.
Published: (2025)

Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
by: Pengmei, Zihan, et al.
Published: (2025)

Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization
by: Liu, Jun
Published: (2026)

Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
by: Egenlauf, Patrick, et al.
Published: (2025)

Causal Anomaly Detection for Lithium-Ion Battery Degradation
by: Heermann, Dieter W., et al.
Published: (2026)

Statistical Mechanics of Dynamical System Identification
by: Klishin, Andrei A., et al.
Published: (2024)

Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
by: Sanokowski, Sebastian, et al.
Published: (2025)

IsingFormer: Augmenting Parallel Tempering With Learned Proposals
by: Bunaiyan, Saleh, et al.
Published: (2025)

Effects of structural properties of neural networks on machine learning performance
by: Arya, Yash, et al.
Published: (2025)