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 …
Tag: excited states
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 »
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.
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 …
Theory was instrumental in rationalizing complex photophysical phenomena experimentally observed for a series of spiro-bridged heterotriangulenes in solution and their aggregates.
We demonstrate that deep learning can be used to perform pure machine learning nonadiabatic excited-state dynamics of molecular systems.
Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.