Quantum machine learning for electronic structure calculations

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC appro...

Wavelets for electronic structure calculations

In 2005, the EU FP6-STREP-NEST BigDFT project funded a consortium of four laboratories, with the aim of developing a novel approach for Density Functional Theory (DFT) calculations based on Daubechies wavelets. Rather than simply building a DFT code from scratch, the objective of this three-years project was to test the potential benefit of a new formalism in the context of electronic structure...

Relativistic electronic-structure calculations for molecules

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Spectral density functionals for electronic structure calculations

We introduce a spectral density-functional theory which can be used to compute energetics and spectra of real strongly correlated materials using methods, algorithms, and computer programs of the electronic structure theory of solids. The approach considers the total free energy of a system as a functional of a local electronic Green function which is probed in the region of interest. Since we ...

Direct Minimization for Ensemble Electronic Structure Calculations