AIQM2 got published in Chemical Science! This ML method’s high speed, competitive accuracy, and robustness enable …
Chemical Science: “AIQM2: Organic Reaction Simulations Beyond DFT” Read more →
Our recent article in npj Computational Materials presents an efficient ML protocol for accelerating trajectory surface …
Are universal machine learning potentials for excited states possible? Such a potential would be a major …
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 …
Postdoc for AI-enhanced atomistic simulations in Dral’s group Read more →
Another year flew by like a comet. And what a year it was! This year was …
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 …
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. …
Adv. Sci.: The Best DFT Functional Is the Ensemble of Functionals Read more →
Recently, we published a paper in JOC about the surprising dynamics phenomena in the Diels–Alder reaction …
JOC: Surprising dynamics phenomena in the Diels–Alder reaction of C60 uncovered with AI Read more →
Recently, we published a paper in JCTC about the end-to-end physics-informed active learning with data-efficient construction of machine …
JCTC: Physics-informed active learning for accelerating quantum chemical simulations Read more →
The work “Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics” performed in …
Physically-consistent quantum dissipative dynamics simulations with neural networks Read more →