Michael Plainer
@plainer.bsky.social
PhD student @ ELIZA TU/FU Berlin - plainer.dev
(7/n) Check out our paper and code:
Paper: arxiv.org/abs/2506.17139
Code + models: github.com/noegroup/Sco...
And also, our self-contained notebooks!
Colab (JAX):
colab.research.google.com/drive/1r3DGO...
Colab (PyTorch):
colab.research.google.com/drive/1rbcND...
#NeurIPS2025 #Diffusion #MD
Paper: arxiv.org/abs/2506.17139
Code + models: github.com/noegroup/Sco...
And also, our self-contained notebooks!
Colab (JAX):
colab.research.google.com/drive/1r3DGO...
Colab (PyTorch):
colab.research.google.com/drive/1rbcND...
#NeurIPS2025 #Diffusion #MD
Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
In recent years, diffusion models trained on equilibrium molecular distributions have proven effective for sampling biomolecules. Beyond direct sampling, the score of such a model can also be used to ...
arxiv.org
November 6, 2025 at 2:41 PM
(7/n) Check out our paper and code:
Paper: arxiv.org/abs/2506.17139
Code + models: github.com/noegroup/Sco...
And also, our self-contained notebooks!
Colab (JAX):
colab.research.google.com/drive/1r3DGO...
Colab (PyTorch):
colab.research.google.com/drive/1rbcND...
#NeurIPS2025 #Diffusion #MD
Paper: arxiv.org/abs/2506.17139
Code + models: github.com/noegroup/Sco...
And also, our self-contained notebooks!
Colab (JAX):
colab.research.google.com/drive/1r3DGO...
Colab (PyTorch):
colab.research.google.com/drive/1rbcND...
#NeurIPS2025 #Diffusion #MD
November 6, 2025 at 2:41 PM
(5/n) With this, we can run coarse-grained Langevin dynamics directly, without the need for any priors or force labels.
This works across biomolecular systems including fast-folding proteins like Chignolin and BBA.
Here is a comparison with and without our regularization:
This works across biomolecular systems including fast-folding proteins like Chignolin and BBA.
Here is a comparison with and without our regularization:
November 6, 2025 at 2:41 PM
(5/n) With this, we can run coarse-grained Langevin dynamics directly, without the need for any priors or force labels.
This works across biomolecular systems including fast-folding proteins like Chignolin and BBA.
Here is a comparison with and without our regularization:
This works across biomolecular systems including fast-folding proteins like Chignolin and BBA.
Here is a comparison with and without our regularization:
(4/n) Our solution:
We train an energy-based diffusion model and regularize it to satisfy the Fokker–Planck equation.
This enforces consistency between:
- The density recovered via denoising
- The potential energy learned at t = 0
Result: the same model can be used for sampling AND simulation.
We train an energy-based diffusion model and regularize it to satisfy the Fokker–Planck equation.
This enforces consistency between:
- The density recovered via denoising
- The potential energy learned at t = 0
Result: the same model can be used for sampling AND simulation.
November 6, 2025 at 2:41 PM
(4/n) Our solution:
We train an energy-based diffusion model and regularize it to satisfy the Fokker–Planck equation.
This enforces consistency between:
- The density recovered via denoising
- The potential energy learned at t = 0
Result: the same model can be used for sampling AND simulation.
We train an energy-based diffusion model and regularize it to satisfy the Fokker–Planck equation.
This enforces consistency between:
- The density recovered via denoising
- The potential energy learned at t = 0
Result: the same model can be used for sampling AND simulation.
(3/n) The root issue is that at very small diffusion times, diffusion models are inaccurate.
The loss is large, and the models violate the Fokker-Planck equation, meaning the evolution of the model’s density and its score disagree.
When that happens, the recovered energy 𝑼(x) is not meaningful.
The loss is large, and the models violate the Fokker-Planck equation, meaning the evolution of the model’s density and its score disagree.
When that happens, the recovered energy 𝑼(x) is not meaningful.
November 6, 2025 at 2:41 PM
(3/n) The root issue is that at very small diffusion times, diffusion models are inaccurate.
The loss is large, and the models violate the Fokker-Planck equation, meaning the evolution of the model’s density and its score disagree.
When that happens, the recovered energy 𝑼(x) is not meaningful.
The loss is large, and the models violate the Fokker-Planck equation, meaning the evolution of the model’s density and its score disagree.
When that happens, the recovered energy 𝑼(x) is not meaningful.
(2/n) The problem: classical diffusion models learn scores that reproduce equilibrium samples, but the corresponding energy-based parameterization is not consistent.
So if you try to use the learned energy to derive forces, the dynamics are wrong, even if the samples themselves look fine.
So if you try to use the learned energy to derive forces, the dynamics are wrong, even if the samples themselves look fine.
November 6, 2025 at 2:41 PM
(2/n) The problem: classical diffusion models learn scores that reproduce equilibrium samples, but the corresponding energy-based parameterization is not consistent.
So if you try to use the learned energy to derive forces, the dynamics are wrong, even if the samples themselves look fine.
So if you try to use the learned energy to derive forces, the dynamics are wrong, even if the samples themselves look fine.