Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry

Clay minerals and their gallery guests: an ab initio investigation into their interactions.

Clay minerals are ubiquitous and readily accessible in the natural environment and consequently have become an essential ingredient in the development of Western Society. Their structural properties are responsible for many of their uses, their layered-leaf composition enables the absorption of water and other solutes, for example. In this thesis, the focus of interest lies primarily in the chemical properties of the clay minerals, which is due to the large surface areas of varying atomistic environments comprising the mineral layers. Clay minerals offer a challenge to the electronic structure modeller as their atomistic composition is non-exact, consequently a number of constraints are automatically applied during the modelling process, the first being the choice of composition of the model. There are currently few examples of density functional theory studies using planewaves and the pseudopotential approximation, and the available experimental data is not necessarily directly applicable to theoretical data due in part, to the inexactness of the clay mineral composition. Consequently, in the studies presented in this thesis, as much time has been spent in considering the modelling methods as on the results obtained and the implication of these in the modelling environment chosen. This thesis records investigations into the decarboxylation of a fatty acid into an alkane and CO$_{2}$ with the modelling of a catalytic environment of an aluminium-bearing clay mineral; the identification of a transition state of this reaction pathway using lattice dynamics and finally, the mechanism of reduction within iron-bearing clay minerals

Annual Report 2014 of the Institute for Nuclear and Energy Technologies (KIT Scientific Reports ; 7702)

The annual report of the Institute for Nuclear and Energy Technologies of KIT summarizes its research activities and provides some highlights of each working group, like thermal-hydraulic analyses for nuclear fusion reactors, accident analyses for light water reactors, and research on innovative energy technologies: liquid metal technologies for energy conversion, hydrogen technologies and geothermal power plants. The institute has been engaged in education and training in energy technologies