Viktor Zaverkin
@viktorzaverkin.bsky.social
Research Scientist @ NEC Labs Europe, Ph.D. in Theoretical Chemistry @ SimTech & @unistuttgart.bsky.social, ML/DL for Chemistry & Materials Science
I’ll be at the Psi-k conference next week! Let’s chat about ML potentials, uncertainty quantification (ensemble-free, gradient-based: Laplace approx., NTKs, batch selection, …), uncertainty-biased MD, message-passing architectures, particle-mesh long-range methods, etc.
🚨 New preprint: How well do universal ML potentials perform in biomolecular simulations under realistic conditions? There's growing excitement around ML potentials trained on large datasets. But do they deliver in simulations of biomolecular systems? It’s not so clear. 🧵 1/
📈My first PhD paper just reached 100 citations, which is a small but very special milestone for me! Our paper introduces Gaussian moments as molecular descriptors and uses them to build ML potentials with an impressive balance between accuracy and computational efficiency.
🚀 Apply Now: International Master's Chemical Sciences! 🌍 The application portal for the English-conducted M.Sc. Chemical Sciences at @unistuttgart.bsky.social are officially open! 🔬✨ 👉 Visit our program website for further details: www.uni-stuttgart.de/en/study/stu...
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I'll present our paper in the afternoon poster session at 4:30pm - 7:30 pm in East Exhibit Hall A-C, poster 3304!
David Holzmüller@dholzmueller.bsky.social · 2y agoCan deep learning finally compete with boosted trees on tabular data? 🌲 In our NeurIPS 2024 paper, we introduce RealMLP, a NN with improvements in all areas and meta-learned default parameters. Some insights about RealMLP and other models on large benchmarks (>200 datasets): 🧵
Neural surrogates can accelerate PDE solving but need expensive ground-truth training data. Can we reduce the training data size with active learning (AL)? In our NeurIPS D3S3 poster, we introduce AL4PDE, an extensible AL benchmark for autoregressive neural PDE solvers. 🧵
Join us today at #NeurIPS2024 for our poster presentation: Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing 🗓️ When: Wed, Dec 11, 11 a.m. – 2 p.m. PST 📍 Where: East Exhibit Hall A-C, Poster #4107 #MachineLearning #InteratomicPotentials #Equivariance #GraphNeuralNetworks
Viktor Zaverkin@viktorzaverkin.bsky.social · 2y ago📣 Can we go beyond state-of-the-art message-passing models based on spherical tensors such as #MACE and #NequIP? Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs. Paper: arxiv.org/abs/2405.14253 Code: github.com/nec-research...
My take: Folks from deep learning (I am guilty too): super excited about methods, modelling, make it look mathy, our number is bold in the table that everyone re-uses for 5 years... Downstream users of tools in drug discovery: Oh god - is that how you evaluated? *Surprised Pikachu meme*
Rommie Amaro@rommieamaro.bsky.social · 2y ago@dereklowe.bsky.social honing in on the same bottom line message from @wpwalters.bsky.social @prof-ajay-jain.bsky.social it's so true and hits so hard:
📣 Can we go beyond state-of-the-art message-passing models based on spherical tensors such as #MACE and #NequIP? Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs. Paper: arxiv.org/abs/2405.14253 Code: github.com/nec-research...