Category: News

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 …

Self-Correcting Machine Learning and Structure-Based Sampling Read More »

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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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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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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 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 …

OMx Methods Score Well in Set from SAMPL5 Challenge Read More »

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

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

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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 …

Highlight about Δ-ML Approach Most Viewed in 2015 Read More »

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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 …

Correcting Differences with Machine Learning Read More »

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