Category: News

Self-Correcting Machine Learning and Structure-Based Sampling

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

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The Effect of the Heteroatom in Organic Acceptors with Acridophosphine Scaffold

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.

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Stable Chiral Dithia-Bridged Hetero[4]helicene Radical Cation

A stable axially chiral radical cation of dithia-bridged hetero[4]helicene has been synthesized and analyzed using experimental and theoretical methods.

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Synergy Between Theory and Experiment: Stability of Pyridyl N-Heterotriangulene Ions

The stability of odd- vs even-electron ions derived from pyridine-substituted N-heterotriangulenes has been investigated by both experiment and theory.

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OMx Methods Score Well in Set from SAMPL5 Challenge

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

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Pyridyl N-Heterotriangulenes for Photovoltaics

Erlangen colleagues synthesized a series of N-heterotriangulenes, which can be used for dye-sensitized solar cells and potentially for fluorescent pH sensors.

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Benchmark of Semiempirical Methods

What is the best semiempirical method to use for your system? Find out in the most extensive benchmark study.

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Details on OMx Methods

Details about theory and implementation of up-to-now most advanced semiempirical quantum-chemical methods are published.

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Highlight about Δ-ML Approach Most Viewed in 2015

A highlight by Jan Jensen about the Δ-ML approach proposed by us [1] was the most viewed highlight in Computational Chemistry Highlights in 2015. This marks a pleasant ending of the last-year research on improving accuracy of computationally less demanding

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Correcting Differences with Machine Learning

In our recent study, we propose using machine learning (ML) to correct differences in properties calculated at two quantum chemical (QC) methods with different accuracy. In the Δ-ML approach ML model is trained on differences between some property calculated at

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