Michael Plainer

[Michael Plainer @michaelplainer]

Excited to share our latest preprint: 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗛𝗮𝗺𝗶𝗹𝘁𝗼𝗻𝗶𝗮𝗻 𝗙𝗹𝗼𝘄 𝗠𝗮𝗽𝘀: 𝗠𝗲𝗮𝗻 𝗙𝗹𝗼𝘄 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗳𝗼𝗿 𝗟𝗮𝗿𝗴𝗲-𝗧𝗶𝗺𝗲𝘀𝘁𝗲𝗽 𝗠𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝗗𝘆𝗻𝗮𝗺𝗶𝗰𝘀 🎉

[Winfried Ripken @RipkenWinfried]

Ever get tired of tiny timesteps bottlenecking your MD simulations?

We show how to train a model for large-timestep Hamiltonian dynamics directly on standard MLFF datasets. 𝗡𝗼 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝗶𝗲𝘀, 𝗻𝗼 𝘂𝗻𝗿𝗼𝗹𝗹𝗶𝗻𝗴, 𝗻𝗼 𝘁𝗲𝗮𝗰𝗵𝗲𝗿 needed!🧵👇

[Michael Plainer @michaelplainer]

@guforosso2 @leonklein26 Unfortunately, there is no free lunch. For the system shown, our model trained about 7 days.

I think this approach is especially promising once it can be transferred. We also trained a model that can simulate all dipeptides, but this is not feasible for larger systems yet ...

[Michael Plainer @michaelplainer]

@trendradar_app @leonklein26 @DoctorYev We compare our results with the ground-truth simulation from the fast-folding protein paper and note close alignment between the two. What I find interesting is that the FES can look a bit oversmoothed, possibly due to the "simple" architecture we use that does not use any priors

[Michael Plainer @michaelplainer]

@giannis_daras @leonklein26 Thank you for sharing this! This indeed looks very related, and we will have a closer look 🙏

[Frank Noe @FrankNoeBerlin]

Fantastic work by Michael Plainer and friends. Energy-based diffusion models to ensure that the denoising distribution equals exp(-u(x)), with the energy u(x). A keystone for connecting molecular dynamics, statistical mechanics and generative AI.

[Leon Klein @leonklein26]

(1/n) Can diffusion models simulate molecular dynamics instead of generating independent samples?

In our NeurIPS2025 paper, we train energy-based diffusion models that can do both:
- Generate independent samples
- Learn the underlying potential 𝑼
🧵👇
https://t.co/TSurVY3YEl