David Rosenberger

Physical mechanisms of nanoparticle–membrane interactions: A coarse-grained study Nanoparticles are promising drug carriers for targeted therapies, diagnostic imaging, and advanced vaccines. However, their clinical translation is limited by c

We uncover the physical mechanisms governing nanoparticle–membrane interactions using coarse-grained simulations, mapping distinct regimes from adhesion to wrapping. A step toward predictive design rules for nanomedicine & adaptive materials

pubs.aip.org/aip/jcp/arti...

Hopefully things will not get to Messi

The original manuscript for Jorgensen’s seminal study.

"It is not a stretch to say that every pharmaceutical company now studying cancer, HIV-AIDS, COVID-19, and a host of other diseases has made use of Jorgensen’s water models."

As we all do, 45k citations and counting! 👏🏻👏🏻👏🏻

news.yale.edu/2025/11/18/d...

„Nach Adeyemi Schock: Dreifacher Undav verhindert KO-Schlag für den VFB“

Peering inside the black box by learning the relevance of many-body functions in neural network potentials - Nature Communications Machine-learned force fields are becoming increasingly popular but suffer from their “black-box” nature. Here the authors adapt explainable AI techniques to coarse-grained graph neural network potenti...

Our latest paper on the interpretation on Neural Network Potentials is now published on Nature Communications: nature.com/articles/s41.... Congratulations to Klara Bonneau, Jonas Lederer and all authors. In collaboration with Klaus-Robert Müller's group.

Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study Argyrodite-type Ag-based sulfides combine exceptionally low lattice thermal and high ionic conductivity, making them promising candidates for thermoelectric and solid-state energy applications. In thi...

A new preprint and two firsts:
Joana Bustamante's first first-author preprint in our group and us developing a model for thermal conductivity!

Feedback welcome!

arxiv.org/abs/2510.23133

#compchem

We (@sobuelow.bsky.social) developed AF-CALVADOS to integrate AlphaFold and CALVADOS to simulate flexible multidomain proteins at scale

See preprint for:
— Ensembles of >12000 full-length human proteins
— Analysis of IDRs in >1500 TFs

📜 doi.org/10.1101/2025...
💾 github.com/KULL-Centre/...

Figure 1 from the review. Caption: Comparison of a schematic example showing static, time-dependent, and time-resolved experiments illustrated by a protein folding process. (a) A static experiment measuring the observable O$_{\text{exp}}$ is shown, which can be modelled as a distribution of simulated values, O$_{\text{calc}}$, representing a conformational ensemble of folded and unfolded states. (b) Shows a time-dependent experiment, where the equilibrium dynamics of reversible folding gives rise to measured transition times $\tau_1$ and $\tau_2$. These can be modelled as equilibrium dynamics, illustrated by a free energy (FE) surface along a chosen degree of freedom (D.O.F.) (c) A time-resolved experiment probes a non-equilibrium process, where the system begins at $t_{0}$ in the folded state. During the observation time $t$ the protein unfolds until $t_{\text{max}}$. At each time point, a distinct ensemble average, O$_{\text{exp}}$, can be observed, reflecting the proteins changing structure. This evolution can be modelled as distributions of O$_{\text{calc}}$ at each time point. These are shown together with a FE surface.

Integrative modelling of biomolecular dynamics

Time-dependent and -resolved experiments combined with computation provide a view on molecular dynamics beyond that available from static, ensemble-averaged experiments

Review w @dariagusew.bsky.social & Carl G Henning Hansen
doi.org/10.48550/arX...

Google Colab

Colab Version from @sokrypton.org colab.research.google.com/github/sokry...

Tenure-track Position in Biophysics at Carnegie Mellon University, Department of Physics Location: Pittsburgh, PA Open Date: Sep 19, 2025 Description The Department of Physics at Carnegie Mellon University invites applications for a tenure-track faculty position in biophysics. The appointment is intended to be at the Assistant Professor level, but exceptional candidates at a higher level may also be considered. We seek outstanding candidates with a strong record in cellular and subcellular biophysics. Topics of particular interest include, but are not limited to, uncovering how key characteristics of living systems arise from the interplay between supramolecular cellular structures, how the emergent cellular circuitry defines goals and enables robust decision making, and how metabolic resources are allocated. This encompasses understanding of how information is learned, stored, transduced, and processed across subcellular structures. Applicants with theoretical, data science, or experimental backgrounds within biological physics are encouraged to apply. The ideal candidate will strengthen and extend research programs of current biophysics faculty in the Department of Physics and collaborate with broader life science activities across many departments at CMU and the wider Pittsburgh area. More details on Interfolio: https://apply.interfolio.com/174360

I am super excited to announce that we have a tenure-track faculty position in biophysics open in the Department of Physics at Carnegie Mellon! 🧪

Interfolio link: apply.interfolio.com/174360

PLEASE, share widely across the blue skies!

Let me briefly explain what we're looking for:

1/10

Apple now has a protein folding NN?...

arxiv.org/pdf/2509.18480

Die deutsche Nationalmannschaft aus der Irrelevanz zum Welt- und Europameister gemacht, dabei als Anführer immer dazu gelernt und gewachsen: Dennis Schröder ist sportartenübergreifend einer der größten deutschen Athleten ever. Punkt.

Who invented convolutional neural networks? Who invented convolutional neural networks?

Very interesting:
people.idsia.ch/~juergen/who...

Can simple exchange heuristics guide us in predicting magnetic properties of solids? A popular heuristic derived from the Kanamori-Goodenough-Anderson rules of superexchange connects bond angles and magnetism in certain transition metal compounds. We evaluate the fulfillment of this h...

Interested in predicting magnetism in transition metal compounds? We have written a paper on how to use exchange heuristics in such models. We also show limits of current theoretical approaches.
Please find our preprint here.
doi.org/10.26434/che...

#compchemsky

Navigating protein landscapes with a machine-learned transferable coarse-grained model - Nature Chemistry The development of a universal protein coarse-grained model has been a long-standing challenge. A coarse-grained model with chemical transferability has now been developed by combining deep-learning m...

Our development of machine-learned transferable coarse-grained models in now on Nat Chem! doi.org/10.1038/s415...
I am so proud of my group for this work! Particularly first authors Nick Charron, Klara Bonneau, Aldo Pasos-Trejo, Andrea Guljas.

The atomate2 paper is finally out: pubs.rsc.org/en/content/a...

Workflows for computational materials science that are ready to be used!!!

#compchem

🚀 After two+ years of intense research, we’re thrilled to introduce Skala — a scalable deep learning density functional that hits chemical accuracy on atomization energies and matches hybrid-level accuracy on main group chemistry — all at the cost of semi-local DFT ⚛️🔥🧪🧬

Some will say yet another league title for Bayern, but they won it with two players with Tottenham DNA… must be the biggest accomplishment in modern football #fcb #miasanmia

AlphaFold is amazing but gives you static structures 🧊

In a fantastic teamwork, @mcagiada.bsky.social and @emilthomasen.bsky.social developed AF2χ to generate conformational ensembles representing side-chain dynamics using AF2 💃

Code: github.com/KULL-Centre/...
Colab: github.com/matteo-cagia...

DFB Pokal, du geiler #BIEB04

SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in re...

How can we improve the synthesizability prediction of inorganic materials? In our new paper, Sasan Amariamir, me, and Philipp Benner suggest so-called co-training (i.e., using the power of two different model architectures)!

Just out in Digital Discovery:
doi.org/10.1039/D4DD...

#compchem

CECAM school on automated ab initio calculations came to an end.

Nearly all teaching material including videos of our atomate2 school is already or will be online:

www.cecam.org/workshop-det...

#compchem

@virtualatoms.bsky.social @naikaakash.bsky.social and many more not on here 😀

Champions League Sieger Stand jetzt

Das Wort zum Mittwoch.

FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events - Nature Communications FIORA, an advanced graph neural network, enhances the simulation of tandem mass spectra by learning molecular bond-breaking patterns. The open-source algorithm generates high-quality reference spectra...

Our paper on FIORA is now officially published in @naturecomms.bsky.social! 🔓Peer-reviewed and ready to shake up mass spec predictions. ⚗️🔨💻📈

Github: github.com/BAMeScience/...
Paper: www.nature.com/articles/s41...

Many thanks to everyone involved 🙌 #MachineLearning #MassSpec #Metabolomics #FIORA

Velocity Jumps for Molecular Dynamics We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical Langevin diffusion and a piecewise deterministic Markov process, where the expensive computation of long-range pairwise interactions is replaced by a resampling of the velocities at random times. This framework allows for an acceleration in the simulation speed while preserving sampling and dynamical properties such as the diffusion constant. It can also be integrated in classical multi-time-step methods, pushing further the computational speedup, while avoiding some of the resonance issues of the latter thanks to the random nature of jumps. The JUMP, JUMP-RESPA and JUMP-RESPA1 integrators have been implemented in the GPU-accelerated version of the Tinker-HP package and are shown to provide significantly enhanced performances compared to their BAOAB, BAOAB-RESPA and BAOAB-RESPA1 counterparts, respectively.

#compchem New paper published in JCTC:
"Velocity Jumps for Molecular Dynamics"
pubs.acs.org/doi/10.1021/...
We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics. Amazing work by Nicolai Gouraud. #compchemsky

🚨 Postdoc Opportunity #2 🚨

We are looking for candidates with a strong background in molecular dynamics simulations of membrane protein interactions to unravel the role of lipids in CD95 oligomerization and signaling!

Please apply by March 28!

#LipidTime @bzh-hd.bsky.social

Excited to annouce that I‘m now working in the eSciene group at @bamresearch.bsky.social Looking forward to explore new frontiers in machine learning and materials.

Stand with UKRAINE! #standWithUkraine

Structural biology is in an era of dynamics & assemblies but turning raw experimental data into atomic models at scale remains challenging. @minhuanli.bsky.social and I present ROCKET🚀: an AlphaFold augmentation that integrates crystallographic and cryoEM/ET data with room for more! 1/14.