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Temperature steerable flows and Boltzmann generators

Manuel Dibak ^1,*, Leon Klein ^1,*, Andreas Krämer ¹, and Frank Noé ^1,2,3,4,†

¹Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany
²Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany
³Department of Chemistry, Rice University, Houston, Texas 77005, USA
⁴Microsoft Research, Station Road, Cambridge, United Kingdom

^*These authors contributed equally to this work.
^†Corresponding author: frank.noe@fu-berlin.de

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Articles Citing

Temperature steerable flows and Boltzmann generators

Breaking the Barriers of Molecular Dynamics With Deep‐Learning: Opportunities, Pitfalls, and How to Navigate Them

Demonstrating real advantage of machine learning–enhanced Monte Carlo for combinatorial optimization

Consistent projection of Langevin dynamics: Preserving thermodynamics and kinetics in coarse-grained models

Energy-based diffusion generator for efficient sampling of Boltzmann distributions

Marginal Girsanov Reweighting: Stable Variance Reduction for Long-Time-Scale Dynamics from Biased Simulation

Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data

Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data

Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications

No title available

Operator forces for coarse-grained molecular dynamics

Learning mappings between equilibrium states of liquid systems using normalizing flows

Deep Generative Modeling of the Canonical Ensemble with Differentiable Thermal Properties

Roadmap on machine learning glassy dynamics

Flow matching for accelerated simulation of atomic transport in crystalline materials

Flow perturbation to accelerate Boltzmann sampling

Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States

Thermodynamic Interpolation: A Generative Approach to Molecular Thermodynamics and Kinetics

Reaction coordinate flows for model reduction of molecular kinetics

AdvNF: Reducing mode collapse in conditional normalising flows using adversarial learning

Energy-efficient and quality-focused control of conveyor belt dryers in petrochemical production

Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics

Efficient mapping of phase diagrams with conditional Boltzmann Generators

IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning

Machine Learning in Soft Matter: From Simulations to Experiments

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

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