Leonardo Medrano

@lmedranos88.bsky.social

Computational physicist/chemist at Dresden University of Technology, Germany | Chemical Physics, Machine learning, NanoPhononics, Computational modeling, Materials Science 🇵🇪

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

Berkeley Lab Computing Sciences@cs.lbl.gov · last yr.

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

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

👋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

nature.com