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
Pinned
🚨 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/
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/
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.
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.
August 24, 2025 at 12:37 PM
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.
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/
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/
August 15, 2025 at 8:30 AM
🚨 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/
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.
Our paper introduces Gaussian moments as molecular descriptors and uses them to build ML potentials with an impressive balance between accuracy and computational efficiency.
June 25, 2025 at 2:42 PM
📈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.
Our paper introduces Gaussian moments as molecular descriptors and uses them to build ML potentials with an impressive balance between accuracy and computational efficiency.
Reposted by Viktor Zaverkin
🚀 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...
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...
Chemical Sciences M.Sc. for prospective students | Study program | University of Stuttgart
Information for prospective students: Chemical Sciences at the University of Stuttgart: application, admission, requirements.
www.uni-stuttgart.de
February 3, 2025 at 7:11 AM
🚀 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...
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...
Reposted by Viktor Zaverkin
I'll present our paper in the afternoon poster session at 4:30pm - 7:30 pm in East Exhibit Hall A-C, poster 3304!
Can 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): 🧵
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): 🧵
December 12, 2024 at 6:54 PM
I'll present our paper in the afternoon poster session at 4:30pm - 7:30 pm in East Exhibit Hall A-C, poster 3304!
Reposted by Viktor Zaverkin
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. 🧵
December 11, 2024 at 6:22 PM
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
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
📣 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...
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
December 11, 2024 at 3:38 PM
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
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
Reposted by Viktor Zaverkin
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*
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*
@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:
it's so true and hits so hard:
December 7, 2024 at 7:03 AM
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*
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*
📣 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...
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
December 6, 2024 at 2:45 PM
📣 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...
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...