The book “Quantum Chemistry in the Age of Machine Learning” guides aspiring beginners and specialists in this exciting field by covering topics ranging from basic concepts to comprehensive methodological details in machine learning, quantum chemistry, and their combinations in a …
Book “Quantum Chemistry in the Age of Machine Learning” Read more »
MLatom@XACS team introduced how to use machine learning in chemistry in the CECAM Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn] school. This school aimed at offering state-of-the-art training in quantum molecular dynamics (QMD), machine learning (ML), and quantum computing (QC) to early-stage …
Tutorial on ML in CECAM school MLQCDyn featuring MLatom@XACS Read more »
We used our MLatom package to develop a machine learning approach for designing materials based on mixed metal halides to facilitate ammonia separation and storage.
In the work published in the Journal of Physical Chemistry Letters, we have proposed a one-shot trajectory learning (OSTL) approach that allows an ultrafast prediction of 10-ps-long quantum dynamics of an open quantum system just in 70 milliseconds. OSTL approach takes …
One-Shot Trajectory Learning of Open Quantum Systems Dynamics Read more »
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 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 …