AI-enhanced computational chemistry
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 …
Data is cornerstone of MLatom as it is a data-driven package. To truly unlock the potential of MLatom, you need to master handling of data with its Python API. In our online tutorial, we give a primer into MLatom’s data …
Not sure which DFT functional to choose for your calculations? Choose them all! (Or at least as many as you can)…To be precise, use the ensembles of the DFT functionals as we recently suggested. These ensembles combine predictions from multiple …
Not sure which DFT functional to choose? Choose them all! Read more »
We have been asked countless times when MLatom can support the periodic boundary conditions (PBC). Finally, we have released the new version of MLatom supporting the calculations with periodic boundary conditions! To get it, you can either use MLatom on …