Quantum Machine Learning Applied to Chemical Reaction Space

The scope of this thesis is the application of quantum machine learning (QML) methods to problems in quantum chemistry and chemical compound space, especially chemical reactions. First, QML models were introduced to improve job scheduling of quantum chemistry tasks on small university and large super computing clusters. Using QML based wall time predictions to optimally distribute the workload on a cluster resulted in a significant reduction of the time to solution by up to 90% depending on the type of calculation studied: Ranging from single point calculations, over geometry optimizations, to transition state searches on a variety of levels of theory and basis sets. The main focus of this thesis remains with the navigation through the chemical reaction space using QML models. To train and test these models large, consistent, and carefully evaluated data sets are required. While extensive data sets with experimental results are available, consistent quantum chemical data sets, especially for reactions, are rare in literature. Thus, a dataset for two competing text book reactions E2 and SN2 was generated, reporting thousands of reactant complexes and transition states with different nucleophiles (-H$^{-}$,-F$^{-}$, -Cl$^{-}$, -Br$^{-}$), leaving groups (-F, -Cl, -Br), and functional groups (-H, -NO$_2$, -CN, -CH$_3$, -NH$_2$) on an ethane scaffold. The geometries were obtained on the MP2/6-311G(d) level of theory with subsequent DF-LCCSD/cc-pVTZ single point calculation. However, limited by computational resources, the data set was incomplete. Therefore, reactant to barrier (R2B) machine learning models were introduced to support the data generation and complete the dateset by predicting ~11'000 activation barriers solely using the reactant geometry as input. Using R2B predictions, design rules for chemical reaction channels were derived by constructing decision trees. Furthermore, Hammond's postulate was investigated, showing the limits for its application on reactants far away from the transition state, e.g. conformers. Finally, the geometry relaxation and transition state search solely using machine learned energies and forces was investigated. Trained on 200 reactions, the QML model was able to find 300 transition states, reaching out of sample RMSD of 0.14Å and 0.4Å for reactant geometries and transition states, respectively. Although, relatively large RMSD for the geometries remain, the out of sample MAE of 26.06$\mathrm{cm^{-1}}$ for the transition state frequencies show a well described curvature of the transition state normal modes in agreement with the MP2 reference

Влияние энергии плазменной струи на продукт плазмодинамического синтеза в системе SI-C

Synthesis of silicon carbide is interested due to the presence of a wide range of his unique mechanical, thermal and electrical properties: superhardness, strength, thermal and corrosion resistance, radiation hardness, unique semiconductor characteristics. There is a great number of nano-SiC synthesis techniques, but the unique mentioned properties of the produced SiC cannot be generally realized due to dependence on the synthesis methods. In this connection the development of new simple and productive methods for the direct synthesis of nanodispersed high-quality silicon carbide is an important problem. The paper presents the results of the plasmodynamic synthesis and the ability to control the synthesis process and to change product characteristics by the plasma jet energy. The above method can be realized in a high-speed pulse jet of the dense Si-C. The jet is generated by a pulse (~100 ?s) high-current (~100 A) coaxial magnetoplasma accelerator with graphite electrodes. The synthesized product was analyzed by some modern techniques as X-ray diffraction. The main result of the paper is a demonstration of the capabilities plasmodynamic synthesis of nanosized cubic silicon carbide. Change of the input energy level can influence on phase composition, crystals growth and particle sizes

Совершенствование процедуры проведения внутреннего аудита в ТПУ (Институт природных ресурсов)

Показано, что внутренний аудит является эффективной деятельностью по предоставлению независимых и объективных гарантий, направленных на совершенствование функционирования института с учетом новых преобразований. Установлено, что разработанные мероприятия по совершенствованию процедуры проведения внутреннего аудита в Институте природных ресурсов позволят определить соответствие деятельности и результатов в области качества запланированным мероприятиям, требованиям ИСО 9001:2008

Геохимическая зональность скарново-золоторудных месторождений Западной Сибири

Исследована геохимическая зональность золото-скарновых месторождений Западной Сибири. Выявлено концентрически зональное строение аномальных структур геохимических полей, сопровождающих изученные месторождения. Определены группы концентрирующихся и деконцентрирующихся (по отношению к золоторудным телам) элементов. Установлена тесная пространственная связь золота с комплексом халькофильных элементов-спутников, набор которых может изменяться в ходе эволюции гидротермальной системы. Набор деконцентрирующихся элементов, накапливающихся по периферии рудных тел, в целом стандартный и включает в себя Ni, Co, Cr, V, Ba, Mn. Проведено обсуждение генетических аспектов выявленной геохимической зональности

Математическое моделирование формирования мерцательной аритмии сердца человека

This paper deals with the modeling of the electrical system of the human cardiac tissue. The paper’s aim is creation of the model, which geometrical structure is closed to the actual geometry of the human heart. The processes occurring in the heart muscle are modeled by solving a system of nonlinear differential equations in COMSOL Multiphysics