Jakub Rydzewski

Call for proposals – Ulam NAWA 2026 The Director of the NAWA announces the call for proposals under the Ulam NAWA Programme 2026.

Looking for a postdoc in computational chemistry, machine learning, and enhanced sampling simulations?

The NAWA Ulam 2026 call is open -- reach out to me at jr@fizyka.umk.pl if interested!

nawa.gov.pl/en/nawa/news...

Major AI conference flooded with peer reviews written fully by AI Controversy has erupted after 21% of manuscript reviews for an international AI conference were found to be generated by artificial intelligence.

www.nature.com/articles/d41...

PET-MAD as a lightweight universal interatomic potential for advanced materials modeling - Nature Communications PET-MAD is a fast and lightweight universal machine-learning potential, trained on a small but diverse dataset, that delivers near-quantum accuracy in atomistic simulations for both organic and inorga...

📢 PET-MAD is here! 📢 It has been for a while for those who read the #arXiv, but now you get it preciously 💸 typeset by @natcomms.nature.com Take home: unconstrained architecture + good train set choices give you fast, accurate and stable universal MLIP that just works™️ www.nature.com/articles/s41...

Constructing Generalized Sample Transition Probabilities with Biased Simulations In molecular dynamics (MD) simulations, accessing transition probabilities between states is crucial for understanding kinetic information such as reaction paths and rates. However, standard MD simula...

Great collaboration with Yanbin Wang and @MingChe40113998 from Purdue Uni. -- JCTC just published our paper introducing generalized sample transition probabilities (GSTP) for constructing collective variables from biased MD data.
pubs.acs.org/doi/full/10....

Now available in Journal of Chemical Information and Modeling!
pubs.acs.org/doi/10.1021/...

NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks Unsupervised machine learning has recently gained much attention in the field of molecular dynamics (MD). Particularly, dimensionality reduction techniques have been regularly employed to analyze larg...

Together with my students, we have implemented a PyTorch Lightning package for dimensionality reduction with a parametric version of tSNE. More methods soon!

Paper: arxiv.org/abs/2505.16476
Code: github.com/NeuralTSNE

PLUMED Tutorials: A collaborative, community-driven learning ecosystem In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes

The paper describing our community effort to collect and organize #plumed tutorials has been published in the Journal of Chemical Physics, as part of the Michele Parrinello Festschrift! doi.org/10.1063/5.02...

For those seeking postdoc opportunities in Poland -- NAWA has opened the Ulam program. If you are interested in applying for a scholarship in our group, please contact me!

nawa.gov.pl/en/programy-...

Machine learning of slow collective variables and enhanced sampling via spatial techniques Understanding the long-time dynamics of complex physical processes depends on our ability to recognize patterns. To simplify the description of these processes,

Very happy to announce that our featured review on thermodynamics-informed learning of slow CVs has finally been published in Chemical Physics Reviews as part of the Special Collection on AI and Machine Learning in Chemical and Materials Science!

doi.org/10.1063/5.02...

Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques Understanding the long-time dynamics of complex physical processes depends on our ability to recognize patterns. To simplify the description of these processes, we often introduce a set of reaction co...

Our review on ML, CVs, and enhanced sampling has been accepted in Chem. Phys. Rev. Congratulations to Tugce Gokdemir
on her 1st first-author paper! arxiv.org/abs/2412.20868

Many thanks to Haochuan Chen, Luke Evans, Luigi Bonati, and Omar Valsson for their great feedback!

Spectral Map: Embedding Slow Kinetics in Collective Variables The dynamics of physical systems that require high-dimensional representation can often be captured in a few meaningful degrees of freedom called collective variables (CVs). However, identifying CVs is challenging and constitutes a fundamental problem in physical chemistry. This problem is even more pronounced when CVs need to provide information about slow kinetics related to rare transitions between long-lived metastable states. To address this issue, we propose an unsupervised deep-learning method called spectral map. Our method constructs slow CVs by maximizing the spectral gap between slow and fast eigenvalues of a transition matrix estimated by an anisotropic diffusion kernel. We demonstrate our method in several high-dimensional reversible folding processes.

For anyone interested in spectral maps.

I made a typo in one equation in the original paper published in JCPL. This has now been fixed; see the accompanying correction note.

pubs.acs.org/doi/full/10....

Interested in doing MD simulations of protein-ligand dissociation? Check out our PLUMED tutorial on the new version of maze: www.plumed-tutorials.org/lessons/24/0...

Implementation: github.com/jakryd/plume...

More details about PLUMED Tutorials in our collaborative work: arxiv.org/abs/2412.03595

PLUMED Tutorials: a collaborative, community-driven learning ecosystem In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, ef...

📢 New #preprint describing our community effort to share #plumed tutorials arxiv.org/abs/2412.03595 ! Explore the tutorials at www.plumed-tutorials.org

Faculty of Physics, Astronomy and Informatics - Nicolaus Copernicus University in Toruń Faculty of Physics, Astronomy and Informatics, Uniwersytet Mikołaja Kopernika w Toruniu.

To PhD students -- if you are interested in molecular dynamics and machine learning, you can now apply for a one-month visit to our group!
Mail or DM me for more information.
fizyka.umk.pl/en/prom-eng/