Roundup of 2025
The end of the year is a good point to pause and look back on what happened in the past year. For me, it was certainly one of the busiest years, full of events. We have got two new babies …
Author: Pavlo Dral
JPCL: A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
Just published in JPCL, an accurate approach for machine learning nonadiabatic coupling vectors! The story behind this work is very long, going back many years. When we were developing ML-driven FSSH, we noticed that learning NACs always has worse R^2 …
Chemical Science: “AIQM2: Organic Reaction Simulations Beyond DFT”
AIQM2 got published in Chemical Science! This ML method’s high speed, competitive accuracy, and robustness enable organic reaction simulations beyond what is possible with the popular DFT methods. It can be used for TS structure search and reactive dynamics, often …
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📢 New: Free Academic Access to Computational Chemistry & AI (July 2025 Edition)
We’re glad to share that, following multiple requests, the July 2025 Edition of the hands-on online course Computational Chemistry & AI by Pavlo O. Dral is now available free of charge for academic users. 🎓 đź“š The first edition (June …
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Linux AI Platform Engineer Opening in Nicolaus Copernicus University in Toruń, Poland
Institution: Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in ToruĹ„. Duration: 1–3 years. We are looking for a Linux AI Platform Engineer to work on projects at the intersection of AI, quantum chemistry, and software …
Linux AI Platform Engineer Opening in Nicolaus Copernicus University in ToruĹ„, Poland Read more »
Two Postdoc Openings at Nicolaus Copernicus University in Toruń, Poland
Institution: Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in ToruĹ„. Duration: 1–3 years. We are looking for two excellent postdocs for projects on the intersection of AI, quantum chemistry, and software engineering in the dynamic, …
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npj Comput. Mater.: Efficient Machine Learning Protocol For Accelerating Trajectory Surface Hopping Dynamics
Our recent article in npj Computational Materials presents an efficient ML protocol for accelerating trajectory surface hopping dynamics, while tackling many key issues making machine learning of excited states difficult. The protocol introduces a new machine learning interatomic potential based …
Meet OMNI-P2x — the First Universal ML Potential for Excited States!
Are universal machine learning potentials for excited states possible? Such a potential would be a major breakthrough — enabling key applications like the design of advanced photomaterials. We’ve already seen successful universal potentials for ground states — ANI-1ccx, MACE-OFF, our …
Meet OMNI-P2x — the First Universal ML Potential for Excited States! Read more »
Postdoc for AI-enhanced atomistic simulations in Dral’s group
The Dral’s group is seeking an outstanding postdoc for AI-enhanced atomistic simulations. The position is a unique opportunity to plunge into the frontiers of atomistic research in collaboration with leading experts on related topics.
Highlights of the Year 2024! Last Weekly Update?
Another year flew by like a comet. And what a year it was! This year was packed full of updates from my group and, of course, MLatom, which is at the center of our research. MLatom provides us with a …
Highlights of the Year 2024! Last Weekly Update? Read more »