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Thorben Frank

@thorbenfrank.bsky.social
234 followers 343 following 7 posts

Postdoctoral Researcher @ TU Berlin @BIFOLD Berlin | AI for molecular simulations

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Reposted by Thorben Frank
Winfried Ripken @winfried-ripken.bsky.social · 19/02/2026
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! 🧵👇
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Thorben Frank @thorbenfrank.bsky.social · 05/12/2025
thank you @adrianhill.de! Complex problems require easy solutions!
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Thorben Frank @thorbenfrank.bsky.social · 29/11/2025
If you ever wondered about the pros and cons of Euclidean symmetries in generative models for molecules, stop by at our #NeurIPS poster on Thursday morning 🧬 #AI4Science
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Thorben Frank @thorbenfrank.bsky.social · 08/09/2025
SO3LR is now published in @jacs.acspublications.org ☀️ It’s a pre-trained #ML #forcefield, applicable to #proteins, #glycoproteins and #lipids in explicit water or vacuo. 🧬 Test it right now and run you own simulations 👉 github.com/general-mole... #AI4Science #MachineLearning
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Reposted by Thorben Frank
profvlilienfeld.bsky.social @profvlilienfeld.bsky.social · 17/01/2025
The room I was given to teach #PhysicalChemistry @uoft.bsky.social has no chalkboards 😱 - o tempora, o mores! But then I can just as well record and share my lectures here: youtube.com/playlist?lis...
youtube.com
Introductory Statistical Mechanics - YouTube
CHM328 lectures given at UofT in winter 2025
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Reposted by Thorben Frank
Microsoft Research @msftresearch.bsky.social · 16/01/2025
Microsoft researchers introduce MatterGen, a model that can discover new materials tailored to specific needs—like efficient solar cells or CO2 recycling—advancing progress beyond trial-and-error experiments. www.microsoft.com/en-us/resear...
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Thorben Frank @thorbenfrank.bsky.social · 03/01/2025
Excited to share our latest work on Euclidean fast attention, which enables learning global atomic representations at linear cost! 🔥 The representations describe the distance and orientation between atoms, crucial for modeling molecular systems tinyurl.com/47xud8nr #MachineLearning #AI4Science
tinyurl.com
Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are o...
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Thorben Frank @thorbenfrank.bsky.social · 03/12/2024
would be great if you can add me as well. Thanks :-)
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Reposted by Thorben Frank
Willie Neiswanger @willieneis.bsky.social · 20/11/2024
The first list filled up, so here's a second list of AI for Science researchers on bluesky. Let me know if I missed you / if you'd like to join! bsky.app/starter-pack...
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Thorben Frank @thorbenfrank.bsky.social · 28/11/2024
would like to be added as well. Thx :-)
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Thorben Frank @thorbenfrank.bsky.social · 28/11/2024
I think @ssnio.bsky.social is missing :-)
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