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. While both machine learning approaches enabled us to improve on the electronic properties of the molecules as compared with existing DFTB parametrizations, only by training on the basis function parameters we were able to obtain consistent Hamiltonians and overlap matrices in the physically reasonable ranges or to improve on multiple electronic properties simultaneously.

Automated summary

We have introduced a machine learning workflow to optimize electronic properties in the density functional tight binding method. By training basis function parameters or a spline model, two-center integrals can be generated and used to construct the Hamiltonian and overlap matrices. This approach significantly improves the electronic properties of molecules.