Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations.

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows toward object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create deep modular interfaces that connect big-data workflows and electronic structure codes and explore the diversity of use cases that they can enable.

Machine Learning Enhanced DFTB Method for Periodic Systems: Learning from Electronic Density of States.

Density functional tight binding (DFTB) is an approximate density functional based quantum chemical simulation method with low computational cost. In order to increase its accuracy, we have introduced a machine learning algorithm to optimize several parameters of the DFTB method, concentrating on solids with defects. The backpropagation algorithm was used to reduce the error between DFTB and DFT results with respect to the training data set and to obtain adjusted DFTB Hamiltonian and overlap matrix elements.

Obtaining Electronic Properties of Molecules through Combining Density Functional Tight Binding with Machine Learning.

We have introduced a machine learning workflow that allows for optimizing electronic properties in the density functional tight binding method (DFTB). The workflow allows for the optimization of electronic properties by generating two-center integrals, either by training basis function parameters directly or by training a spline model for the diatomic integrals, which are then used to build the Hamiltonian and the overlap matrices. Using our workflow, we have managed to obtain improved electronic properties, such as charge distributions, dipole moments, and approximated polarizabilities.

A computational study of doped olivine structured CdGeO: local defect trapping of interstitial oxide ions.

Computational modelling techniques have been employed to investigate defects and ionic conductivity in CdGeO. We show due to highly unfavourable intrinsic defect formation energies the ionic conducting ability of pristine CdGeO is extremely limited. The modelling results suggest trivalent doping on the Cd site as a viable means of promoting the formation of the oxygen interstitial defects.