Category: Method Development

Johannes Margraf and I have published our perspective on what semiempirical molecular orbital (SEMO) methods are and should be approximating in the article[1] dedicated to the 70th birthday of our PhD supervisor Tim Clark.

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MLatom 1.0 release of my package for atomistic simulations with machine learning is now available.

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We have introduced two new NDDO-based semiempirical quantum-chemical methods ODM2 and ODM3, which are more consistent and accurate than other existing methods of this type.

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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.

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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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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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We propose using machine learning (ML) for improving semiempirical Hamiltonian. Given sufficiently large training set ML can be used to correct parameters of semiempirical quantum chemical (SQC) method individually for any target molecule. Such automatic parametrization technique (APT) stands in …

Machine Learning of Semiempirical Parameters Read More »

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Did you know that the reactivity of alkyl radicals towards H-abstraction is related to their electron accepting properties? And that alkyl cations are much more reactive than alkyl radicals for the same reason? The same tool that clearly visualizes these …

The Unrestricted Local Properties Read More »

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