Leonardo Medrano
@lmedranos88.bsky.social
150 followers 420 following 11 posts
Computational physicist/chemist at Dresden University of Technology, Germany | Chemical Physics, Machine learning, NanoPhononics, Computational modeling, Materials Science 🇵🇪
Posts Media Videos Starter Packs
Reposted by Leonardo Medrano
luxprovide.bsky.social
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 👉 www.luxprovide.lu/advancing-de...
lmedranos88.bsky.social
🗣️The #ieeenanoperu Chapter, in collaboration with #IEEE Nanotechnology Council, is organizing the first IEEE #LatinAmerican Conference on #Nanotechnology ( #ieeelanano), to be held in the historic city of Cusco, #Peru, from November 4-7, 2025.

👉 ieee-lanano.org

@ieeexplore.bsky.social #IYQ2025
Reposted by Leonardo Medrano
msftresearch.bsky.social
Today in the journal Science: BioEmu from Microsoft Research AI for Science. This generative deep learning method emulates protein equilibrium ensembles – key for understanding protein function at scale. www.science.org/doi/10.1126/...
lmedranos88.bsky.social
As this field is rapidly evolving, we couldn't include all methods recently published.

🙏Thanks to @cecamevents.bsky.social for supporting the workshop “Leveraging #MachineLearning for Sampling #RareEvents in #BiomolecularSystems”, where the discussions that led to this review began.
lmedranos88.bsky.social
We believe this review will serve as a helpful starting point for researchers entering the field of “Machine Learning for Biomolecular Simulations”. Main topics:

-Atomistic ML potentials
-Coarse-grained potentials
-ML for #BiomolecularBackmapping
lmedranos88.bsky.social
🚨Our short review “Recent Advances in #MachineLearning and #CoarseGrained Potentials for #BiomolecularSimulations” has been accepted in @biophysj.bsky.social @cellpress.bsky.social!
Many thanks to all authors for their contributions! This was a fantastic collaboration!👏

www.cell.com/biophysj/ful...
lmedranos88.bsky.social
👋Just two weeks left to register for the #SusML Workshop 2025 in Dresden! susml.net

Have a look at our exceptional lineup of speakers that will discuss current ML methods for the sustainable exploration of chemical spaces.

#Psik @tudresden.bsky.social @digital-discovery.rsc.org @mpipks.bsky.social
lmedranos88.bsky.social
From foundational datasets like QM9, QM7-X, ANI, Aquamarine, and GEOM (among others) to the recently published #QCML and now #TheOpenMolecules2025! The exploration of the #ChemicalSpace through #QuantumMechanical properties has progressed remarkably over the past five years. 😀

#sustainableML
cs.lbl.gov
DYK Open Molecules 2025—an unprecedented molecular simulation dataset—was just released? Co-led by @berkeleylab.lbl.gov & #Meta, this resource could transform #MachineLearning for real-world #chemistry, #biology, & #energy technologies: bit.ly/OMol25

#AI #MaterialScience #Science #Research #HPC
Computational Chemistry Unlocked: A Record-Breaking Dataset to Train AI Models has Launched - Berkeley Lab
Scientists will finally be able to simulate the chemistry that drives our bodies, our environment, and our technologies.
bit.ly
Reposted by Leonardo Medrano
aimsduke.bsky.social
The biggest paper I was ever part of appeared on arXiV today: "Roadmap on Advancements of the FHI-aims Software Package". Over 20 years of work. Immensely grateful to the 200+ people on this paper, who pushed our ability to simulate materials forward! #chemsky #compchemsky

arxiv.org/abs/2505.00125
Roadmap on Advancements of the FHI-aims Software Package
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, relia...
arxiv.org
lmedranos88.bsky.social
🚨Check out our recent #preprint on advancing Density Functional Tight-Binding method with equivariant NNs. We have been developing this project for a while, and we now present the results that highlight the enhanced scalability/transferability of our DFTB+ML approach.

🌐 chemrxiv.org/engage/chemr...
Reposted by Leonardo Medrano
mddbeu.bsky.social
📢 Our article calling for a #FAIR database for #MolecularDynamics simulation data has now been peer-reviewed and published in @naturemethods.bsky.social

📖 Read it here: rdcu.be/ef6YX

📝 Support the statement: bit.ly/3zVS3qm

#MDDB #FAIRdata #collaboration
lmedranos88.bsky.social
👋Hallo there!
The registration for the SusML workshop is OPEN! Join us in discussions on topics such as data-efficient ML-based methodologies and the inverse property-to-structure problem. See you at @tudresden.bsky.social in Germany!

👉More information: susml.net

Stay tuned for more updates!😉
lmedranos88.bsky.social
👋The MORE-Q dataset is finally out in Scientific Data! 😃

We have performed extensive electronic structure calculations to generate #quantumechanical property data for building blocks of mucin-derived olfactory #sensingdevices.

🌐You can read more about MORE-Q at: www.nature.com/articles/s41...
MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics - Scientific Data
Scientific Data - MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics
www.nature.com
lmedranos88.bsky.social
👋Preprint: check out our contribution to this short review where we discuss recent efforts toward developing robust #machinelearning potentials for #biomolecularsimulations with #quantummechanical accuracy. 👀

👉The preprint is available on @chemrxiv.bsky.social: chemrxiv.org/engage/chemr...
Reposted by Leonardo Medrano
profvlilienfeld.bsky.social
Pumped about #MachineLearning the adaptive exact exchange admixture in hybrid #DFT approximations: It can even cure the infamous spin-gap problem (see below). Just out in @ScienceAdvances with D Khan, A Price, B Huang and M Ach! @uoft.bsky.social #CompChem www.science.org/doi/10.1126/...
lmedranos88.bsky.social
🚨The first preprint of the year is out on @chemrxiv.bsky.social! Great collaboration with Mirela, Alexander, Peter et al.!!👍

chemrxiv.org/engage/chemr...

We introduce the QUID benchmark framework for large non-covalent systems, capturing frequent ligand-pocket interaction types.
Reposted by Leonardo Medrano
msftresearch.bsky.social
Microsoft researchers introduce MatterGen, a model that can discover new materials tailored to specific needs—like efficient solar cells or CO2 recycling—advancing progress beyond trial-and-error experiments. www.microsoft.com/en-us/resear...