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In this work we investigate the mechanism of photodesorption of water from a WO3(001) surface by theoretical calculations, applying an embedded cluster model. Using the CASSCF method, we have calculated both the ground state as well as the energetically preferred charge-transfer state in three degrees of freedom of the water molecule on the surface. The calculated potential energy surfaces were afterwards fitted with a neural network optimized by a genetic algorithm. A final quantum dynamic wave packet study provided insight into the photodesorption mechanism.

In this work we use different theoretical approaches to investigate the mechanism of water splitting on (001)-WO3. Using an LCAO approach with periodic boundary conditions, hybrid DFT functionals, ...

Successful and cost-effective water splitting could be one of the most interesting energy sources of the future. The modification of the transition metal oxide rutile-TiO 2 as photocatalyst can lead to improved performance in the water splitting process. For that purpose, an accurate description of the interaction potential of a water molecule and the rutile-TiO 2 (110) surface in the ground and electronically excited state after photoexcitation is crucial. The electronic Schrodinger equation for the states involved is solved pointwise for different nuclear configurations within the Born-Oppenheimer approximation, and accurate fits to these energy points are required to obtain an analytic expression for the potential energy surface. This is too computationally expensive for fine-grained surface calculations of quantum chemical models. In this paper, we propose to use state-of-the-art deep learning techniques to provide accurate fits for this problem. Namely, we employ a fully connected variant of ResNet and DenseNet with heavy regularization (L2, RReLU, Dropout, and BatchNormalization). Previous literature applied neural network approaches before, but with unsatisfactory accuracy. In an experimental evaluation we show that the root mean squared error (RMSE) can be 6.8 times lower for the exited state and 12.7 times lower for the ground state compared to former approaches.

A computational tool integrated into a commercial simulation software (CHEMCAD) is developed for the analysis and synthesis of crystallization processes. The tool utilizes the comprehensive thermodynamic models and the rigorous computational algorithms available. The crystallization calculations are formulated as multicomponent multiphase equilibrium phase calculations and support the analysis of various modes of crystallization, such as cooling, evaporative, and the use of mass separating agents such as antisolvents and cosolvents. The tool is demonstrated with applications related to the crystallization of API’s and LCD crystals.

The α-alkylation of amines with alkenes catalyzed by early transition-metal complexes represents an efficient and atom economic method for the synthesis of functionalized amines from simple and easily available starting materials. While the successful use of secondary amines, such as dimethylamine, strongly underlines the enormous industrial potential of this reaction, the analogous intermolecular α-alkylation of primary amines, especially methylamine, remains an unsolved synthetic task to this day. Based on calculated thermodynamic data, these experimental findings can now be explained for the first time, whereby several competing reactions, which are explained in detail, are of crucial importance for the different behavior of primary and secondary amines.