Waiting for your MD simulation to finish? Visit our spotlight paper at ICML on how to train large timestep models on existing MLFF datasets in the meantime! 🗓️ Wednesday, July 8 ⏰ 10:30 AM – 12:15 PM KST arxiv.org/abs/2601.22123

Winfried Ripken@winfried-ripken.bsky.social · 4w ago

Excited to be at ICML with @plainer.bsky.social & @gregorlied.bsky.social to present our 🌟 spotlight 🌟 paper on Hamiltonian Flow Maps for large-timestep molecular dynamics! Come say hi at our poster tomorrow or send us a message if you want to chat! 🤗 🗓️ July 8, 10:30 AM – 12:15 PM 📍 HALL A #1306

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

Winfried Ripken@winfried-ripken.bsky.social · 6mo ago

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! 🧵👇

(1/n) Can diffusion models simulate molecular dynamics instead of just generating independent samples? In our NeurIPS 2025 paper, we train energy-based diffusion models that can do both: - Generate independent samples - Learn the underlying potential 𝑼 🧵👇 Paper: arxiv.org/abs/2506.17139