No Arabic abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well-suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally observed NMR chemical shifts and those of candidate structures. Chemical shifts for the candidate configurations have traditionally been computed by electronic-structure methods, and more recently predicted by machine learning. However, the reliability of the determination depends on the errors in the predicted shifts. Here we propose a Bayesian framework for determining the confidence in the identification of the experimental crystal structure, based on knowledge of the typical error in the electronic structure methods. We also extend the recently-developed ShiftML machine-learning model, including the evaluation of the uncertainty of its predictions. We demonstrate the approach on the determination of the structures of six organic molecular crystals. We critically assess the reliability of the structure determinations, facilitated by the introduction of a visualization of the of similarity between candidate configurations in terms of their chemical shifts and their structures. We also show that the commonly used values for the errors in calculated $^{13}$C shifts are underestimated, and that more accurate, self-consistently determined uncertainties make it possible to use $^{13}$C shifts to improve the accuracy of structure determinations.
In this PhD thesis, a novel method to determine protein structures using chemical shifts is presented.
The calculation of optimal structures in reaction-diffusion models is of great importance in many physicochemical systems. We propose here a simple method to monitor the number of interphases for long times by using a boundary flux condition as a control. We consider as an illustration a 1-D Allen-Cahn equation with Neumann boundary conditions. Numerical examples are given and perspectives for the application of this approach to electrochemical systems are discussed.
X-ray as well as electron diffraction are powerful tools for structure determination of molecules. Electron diffraction methods yield r{A}ngstrom-resolution even when applied to large systems or systems involving weak scatterers such as hydrogen atoms. For cases in which molecular crystals cannot be obtained or the interaction-free molecular structure is to be addressed, corresponding electron scattering approaches on gas-phase molecules exist. Such studies on randomly oriented molecules, however, can only provide information on interatomic distances, which is challenging to analyse in case of overlapping distance parameters and they do not reveal the handedness of chiral systems8. Here, we present a novel scheme to obtain information on the structure, handedness and even detailed geometrical features of single molecules in the gas phase. Using a loop-like analysis scheme employing input from ab initio computations on the photoionization process, we are able to deduce the three dimensional molecular structure with sensitivity to the position individual atoms, as e.g. protons. To achieve this, we measure the molecular frame diffraction pattern of core-shell photoelectrons in combination with only two ionic fragments from a molecular Coulomb explosion. Our approach is expected to be suitable for larger molecules, as well, since typical size limitations regarding the structure determination by pure Coulomb explosion imaging are overcome by measuring in addition the photoelectron in coincidence with the ions. As the photoelectron interference pattern captures the molecular structure at the instant of ionization, we anticipate our approach to allow for tracking changes in the molecular structure on a femtosecond time scale by applying a pump-probe scheme in the future.
We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization energies of organic molecules have been successful using a Coulomb matrix representation of the molecule. We consider three ways to generalize such representations to periodic systems: (i) a matrix where each element is related to the Ewald sum of the electrostatic interaction between two different atoms in the unit cell repeated over the lattice; (ii) an extended Coulomb-like matrix that takes into account a number of neighboring unit cells; and (iii) an Ansatz that mimics the periodicity and the basic features of the elements in the Ewald sum matrix by using a sine function of the crystal coordinates of the atoms. The representations are compared for a Laplacian kernel with Manhattan norm, trained to reproduce formation energies using a data set of 3938 crystal structures obtained from the Materials Project. For training sets consisting of 3000 crystals, the generalization error in predicting formation energies of new structures corresponds to (i) 0.49, (ii) 0.64, and (iii) 0.37 eV/atom for the respective representations.
This report covers the development of a new, fast method for calculating the backbone amide proton chemical shifts in proteins. Through quantum chemical calculations, structure-based forudsiglese the chemical shift for amidprotonen in protein has been parameterized. The parameters are then implemented in a computer program called Padawan. The program has since been implemented in protein folding program Phaistos, wherein the method andvendes to de novo folding of the protein structures and to refine the existing protein structures.