It has been a great pleasure to give a plenary lecture at the 25th International Annual Symposium on Computational Science and Engineering (ANSCSE25). I was talking about AI/ML in computational chemistry, how our MLatom can help with it, and introduced …
In our work published in the Journal of Physical Chemistry Letters, we investigate the performance of the general-purpose data-driven methods ANI-1ccx and AIQM1 in the calculation of enthalpies of formation. Extensive benchmark tests show that these two methods can achieve …
In the work published in Nature Communications, we have developed a blazingly fast artificial intelligence (AI)-based quantum dynamics (QD) approach with applications to excitation energy transfer in the well-known Fenna–Matthews–Olson (FMO) complex found in green sulfur bacteria.
Our AIQM1 paper is one of the 25 most downloaded Nature Communications articles in chemistry and materials sciences published in 2021! #NCOMTop25 The full list: https://www.nature.com/collections/gagdjjgcgj AIQM1 paper: https://www.nature.com/articles/s41467-021-27340-2 How to use AIQM1 method with MLatom: http://MLatom.com/AIQM1
In the work published in New Journal of Physics, we combine machine learning (ML) with the numerically exact hierarchical equations of motion (HEOM) approach, propagating quantum dynamics of a two-state system (spin-boson model) with only ca. 10% of the HEOM …
Speeding up quantum dissipative dynamics of open systems with kernel methods Read more »
We report global potential energy surfaces (PESs) database VIB5 of 5 molecules of astrophysical interest which was used to produce rovibrational spectra approaching spectroscopic accuracy and contains state-of-the-art, high-level energies and energy gradients. This database can be used to develop …
We have developed artificial intelligence-enhanced quantum mechanical method 1 (AIQM1), which can be used out of the box for very fast quantum chemical calculations with the accuracy of the gold-standard coupled-cluster method. Read more »
Lost in the sea of all machine learning potentials? Our overview and recommendations based on balanced analysis are just out in Chemical Science. In brief, kernel methods are a better choice for not too large data in terms of both …
We are happy to introduce MLatom 2: a major release of our integrative platform for user-friendly atomistic machine learning. It includes many more features and is further optimized for efficiency. Detailed overview of MLatom 2 is given in our contribution …
MLatom 2: Introducing a Platform for Atomistic Machine Learning Read more »
In our Review “Molecular excited states through a machine learning lens” in Nature Reviews Chemistry, we provide insights and highlight challenges of the rapidly growing field of machine learning for excited-states simulation and analysis.