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 »
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 »
Recently, we published a paper in JCTC about the end-to-end physics-informed active learning with data-efficient construction of machine learning potentials. It shortens molecular simulation time to a couple of days which could have taken weeks of pure quantum chemical calculations. The active …
JCTC: Physics-informed active learning for accelerating quantum chemical simulations Read more »
A year ago, we released MLatom 3, making MLatom the fully-fledged Python package. This shift endowed MLatom with much of its current flexibility that also allowed us to rapidly do method development and new implementations: in one year we had …
Delta-learning is a powerful concept that inspired many works of ML in computational chemistry. The idea is simple: add ML correction to the predictions of the baseline low-level QM method to approximate the target high-level QM method. Think about correcting …
Have an idea for a new model and are looking for the right tool? Try MLatom. Because MLatom is easy to use to create new models, it has an impressive collection of rapidly growing number of models. And your model can …
Lego-bricks and infrastructure for your own computational chemistry model Read more »
Two-photon absorption is a fascinating property: some molecules can simultaneously absorb two photons and then emit a photon of higher energy than absorbed ones. This has found numerous uses: As a result, it is very important to find materials with stronger …
Our active learning protocol for accelerating surface hopping dynamics with machine learning is now available in MLatom 3.10! It is efficient and robust: often, you can do surface-hopping dynamics from start to finish within a couple of days on a …