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 »
Do you want to perform computational chemistry simulations but struggle with complex codes and lack computational resources? Are you conducting experiments without theoretical guidance and feel lost? No worries, the help is on the way! We are pleased to announce …
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 »
The new release of MLatom, version 3.16.1, provides an interface to DFTB+ and performance improvements. So even if you are not interested in DFTB, please upgrade your MLatom installation for faster calculations via pip: New release allows seamless execution of …
You can now use MLatom to perform TDDFT and TDA calculations with MLatom and parse Gaussian output files.This can be useful for UV/vis spectra simulations via single-point convolution and nuclear-ensemble approach (NEA).We also welcome a new contributor to MLatom: Vignesh Kumar …
TDDFT and TDA calculations + parsing of Gaussian output files Read more »
The explosion of quantum chemical datasets (see our overview of them) satisfies the appetite of those data-hungry machine learning potentials while raising another critical question: how to learn data in different fidelities? Here, we propose the all-in-one (AIO) ANI model, …
All-in-one: Learning across quantum chemical levels. Better than transfer learning! Read more »
Recently, we published a paper in JOC about the surprising dynamics phenomena in the Diels–Alder reaction of fullerene C60. The AI-accelerated molecular dynamics uncovers that in a small fraction (10%) of reactive trajectories, the diene molecule (2,3-dimethyl-1,3-butadiene) is roaming around …
JOC: Surprising dynamics phenomena in the Diels–Alder reaction of C60 uncovered with AI Read more »
AIQM2 is the long-awaited successor of the highly successful AIQM1 (see Nat. Commun. paper). It has overall improved accuracy and much better performance for transition states, where it produces high-quality geometries and barrier heights: it is better than B3LYP/6-31G* but orders of …
AIQM2 is out: better and faster than B3LYP for reaction simulations! Read more »