TCPUniLu @TCPUniLu
Theoretical Chemical Physics Group of Prof. Alexandre Tkatchenko at the University of Luxembourg @uni_lu tcpunilu.com Luxembourg Joined October 2019-
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TCPUniLu retweeted
Our recent work on SO3LR, a general-purpose machine learned force field for molecular simulations, has been published in @J_A_C_S! 🌞
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TCPUniLu retweeted
🔬New tool for computational spectroscopy! Researchers from @TCPUniLu in collaboration with @JanssenGlobal unveil THeSeuSS, a Python-based platform automating IR & Raman spectra simulations for molecules & solids. Powered by 🇱🇺 #MeluXina supercomputer 👉luxprovide.lu/vibrational-sp…
TCPUniLu retweeted
New on #HPCSummerQuest: EquiDTB blends quantum chemistry with equivariant AI to reach DFT-level accuracy for large, flexible molecules. Trained on #MeluXina GPUs, cutting runtimes from weeks to days. ⚡️🧬🧠 Read more 👉luxprovide.lu/advancing-dens…
TCPUniLu retweeted
Very happy to share our preprint of work led by @HengZ_921 on making DFT accurate for charged systems, with broad implications for biochemistry, enzymology, and materials. Big collaborative effort @nagyrpeter @TCPUniLu @unifrChemistry 🔗 doi.org/10.26434/chemr…
TCPUniLu retweeted
🚨Check out our recent #preprint on advancing Density Functional Tight-Binding method with equivariant NNs. We have been developing this project for a while, the results now highlight the enhanced scalability/transferability of our DFTB+ML approach. 🌐chemrxiv.org/engage/chemrxi…
Check out @AriadniBoziki's talk at #ACSSpring2025: 'Automating polymorph characterization: THeSeuSS for simulating IR, Raman, and THz spectra' on March 26, 10:55 - 11:10 AM in Hall F Room 1.
TCPUniLu retweeted
Atomic Orbits in Molecules and Materials for Improving Machine Learning Force Fields #machinelearning #compchem dx.doi.org/10.26434/chemr…
TCPUniLu retweeted
Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling jcheminf.biomedcentral.com/articles/10.11… #chemoinformatics #feedly
Congrats to @SuperFalla!
Congrats to @SuperFalla!
TCPUniLu retweeted
Alexandre Tkatchenko, from the University of Luxembourg, is giving the 40th NCCR MARVEL Distinguished Lecture today at 4 pm CET (online and at EPFL) on "Realizing Schrödinger's dream with AI-enabled molecular simulations". More info and link 👉 nccr-marvel.ch/events/marvel-…
TCPUniLu retweeted
Learn how to calculate diffusion pathways with FHI-aims and aimsChain with our new 5-min video and accompanying written tutorial: youtu.be/wjQDEE7UCWw Based on the work of @TCPUniLu 🥳
TCPUniLu retweeted
Now published! The paper that was the topic of our recent FHI-aims webinar. In case you missed it, you can see the recordings on our YouTube channel youtube.com/watch?v=vxEnl6… and follow our tutorial to see how these calculations were performed in FHI-aims fhi-aims-club.gitlab.io/tutorials/find…
Now published! The paper that was the topic of our recent FHI-aims webinar. In case you missed it, you can see the recordings on our YouTube channel youtube.com/watch?v=vxEnl6… and follow our tutorial to see how these calculations were performed in FHI-aims fhi-aims-club.gitlab.io/tutorials/find…
TCPUniLu retweeted
Non-local interactions determine local structure and lithium diffusion in solid electrolytes nature.com/articles/s4146…
New paper just out in @NatureComms! Non-local interactions determine local structure and lithium diffusion in solid electrolytes link: doi.org/10.1038/s41467…
TCPUniLu retweeted
🚀Submit your abstract and discover the experts who will be sharing their insights on AI for Physics! As AI advances, it could unlock breakthroughs in dark matter research and fusion energy, pushing the boundaries of human knowledge and exploration.
The first part of the ☕️TEA Challenge, featuring Model Analysis, has now also been published in @ChemicalScience! This concludes the Crash Testing Machine Learning Force Fields TEA Challenge. Congratulations to everyone involved! link: doi.org/10.1039/D4SC06…
The first part of the ☕️TEA Challenge, featuring Model Analysis, has now also been published in @ChemicalScience! This concludes the Crash Testing Machine Learning Force Fields TEA Challenge. Congratulations to everyone involved! link: doi.org/10.1039/D4SC06… https://t.co/cjfWG4oGMA
TCPUniLu retweeted
The recordings of our recent FHI-aims webinar and hands-on session are now online: youtube.com/playlist?list=… Thank you to our great speakers, Mariana Rossi and Alexandre Tkatchenko (@TCPUniLu), as well as everyone who joined and participated in the Q&A and hands-on session!
TCPUniLu retweeted
The second part of the ☕️TEA Challenge is now published in @ChemicalScience! link: doi.org/10.1039/D4SC06…
The second part of the ☕️TEA Challenge is now published in @ChemicalScience! link: doi.org/10.1039/D4SC06… https://t.co/SwmD49IMrb
TCPUniLu retweeted
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023 #machinelearning #compchem pubs.rsc.org/en/Content/Art…
TCPUniLu retweeted
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023 pubs.rsc.org/en/Content/Art…
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