In the work published in New Journal of Physics, we combine machine learning (ML) with the …
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 …
Lost in the sea of all machine learning potentials? Our overview and recommendations based on balanced …
We are happy to introduce MLatom 2: a major release of our integrative platform for user-friendly …
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 …
Review on Machine Learning for Molecular Excited States 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 …
Diazapentacene derivatives were synthesized and investigated for their potential application in organic field-effect transistors, with one …
My book chapter shows in a tutorial way how to use machine learning to assist quantum …
Chapter on Machine Learning in Quantum Chemistry in a Tutorial Way Read more →
Theory was instrumental in rationalizing complex photophysical phenomena experimentally observed for a series of spiro-bridged heterotriangulenes …
Theory Untangles Fascinating Properties of Spiro-Compounds Read more →
We introduced hierarchical machine learning (hML) approach for building highly accurate potential energy surfaces from multiple …