AI-enhanced computational chemistry
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 »
In our latest tutorial we show how MLatom can be used to simulate UV/vis spectra via single-point convolution and nuclear-ensemble approach (NEA). ML can also be used to increase precision of the NEA spectra at reduced cost. You can watch the detailed introduction and demonstration in the video. All …
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 »
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 »
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 …