We comprehensively analyzed the validity of the NDDO (neglect of diatomic differential overlap) approximation, which forms the basis for most modern semiempirical quantum chemical methods.
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.
Structure-based sampling and self-correcting machine learning is used for precise representation of molecular potential energy surfaces and calculating vibrational levels with spectroscopic accuracy (errors less than 1 cm−1 relative to the reference ab initio spectrum) decreasing the number of required …
A series of the substituted two-electron acceptors with a dicyanomethylene-bridged acridophosphine scaffold has been prepared and compared with the nitrogen-containing counterpart using various spectroscopic, electrochemical and theoretical methods.
A stable axially chiral radical cation of dithia-bridged heterohelicene has been synthesized and analyzed using experimental and theoretical methods.
The stability of odd- vs even-electron ions derived from pyridine-substituted N-heterotriangulenes has been investigated by both experiment and theory.
OMx methods have once again been shown to give as accurate results as DFT methods, but substantially faster. The OM2 method has outperformed other semi-empirical methods and has essentially the same accuracy as BLYP for the distribution coefficient part of …
Erlangen colleagues synthesized a series of N-heterotriangulenes, which can be used for dye-sensitized solar cells and potentially for fluorescent pH sensors.
What is the best semiempirical method to use for your system? Find out in the most extensive benchmark study.