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 …
Chemical Science: “AIQM2: Organic Reaction Simulations Beyond DFT” Read more »
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 …
📢 New: Free Academic Access to Computational Chemistry & AI (July 2025 Edition) Read more »
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 »
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, …
Two Postdoc Openings at Nicolaus Copernicus University in ToruĹ„, Poland Read more »
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 …
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 »
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.
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 »
Theoretical IR (infrared) spectroscopy is a powerful tool for assisting chemical structure identification. However, approaches based on quantum chemical calculations suffer from either high computational cost (e.g., density functional theory, DFT) or insufficient accuracy (semi-empirical methods). Hence, we introduce a new …
ML-enhanced Fast and Interpretable Simulation of IR Spectra Read more »
Density functional theory (DFT) methods are by far the most popular approaches for electronic structure calculations. However, the “best” functional remains elusive despite the increasing variety of functionals and continuous efforts to improve their computational accuracy. In our work published in Advanced …
Adv. Sci.: The Best DFT Functional Is the Ensemble of Functionals Read more »