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 »

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 »

Paper Bao-Xin Xue, Mario Barbatti*, Pavlo O. Dral*, Machine Learning for Absorption Cross Sections, J. Phys. Chem. A 2020, 124, 7199–7210. DOI: 10.1021/acs.jpca.0c05310.Preprint on ChemRxiv, DOI: 10.26434/chemrxiv.12594191. Short overview of the method in a form of LiveSlides: In brief ML-NEA can boost the calculation speed and increase …

Machine Learning for Absorption Cross Sections Read more »

A post-doctoral position is open in the group of Dr. Pavlo Dral in College of Chemistry and Chemical Engineering at Xiamen University for the development of cutting-edge machine learning and quantum chemistry methods. The earliest starting date is January 2020. …

Post-doctoral Position Opening in Machine Learning in Quantum Chemistry Read more »