383 research outputs found
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, -CN, -CH, -NH) 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 0.14Γ
and 0.4Γ
for reactant geometries and transition states, respectively.
Although, relatively large RMSD for the geometries remain, the out of sample MAE of 26.06 for the transition state frequencies show a well described curvature of the transition state normal modes in agreement with the MP2 reference
ΠΠ»ΠΈΡΠ½ΠΈΠ΅ ΡΠ½Π΅ΡΠ³ΠΈΠΈ ΠΏΠ»Π°Π·ΠΌΠ΅Π½Π½ΠΎΠΉ ΡΡΡΡΠΈ Π½Π° ΠΏΡΠΎΠ΄ΡΠΊΡ ΠΏΠ»Π°Π·ΠΌΠΎΠ΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ½ΡΠ΅Π·Π° Π² ΡΠΈΡΡΠ΅ΠΌΠ΅ 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
Π‘ΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π²Π½ΡΡΡΠ΅Π½Π½Π΅Π³ΠΎ Π°ΡΠ΄ΠΈΡΠ° Π² Π’ΠΠ£ (ΠΠ½ΡΡΠΈΡΡΡ ΠΏΡΠΈΡΠΎΠ΄Π½ΡΡ ΡΠ΅ΡΡΡΡΠΎΠ²)
ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Π²Π½ΡΡΡΠ΅Π½Π½ΠΈΠΉ Π°ΡΠ΄ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΡΡ ΠΏΠΎ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΡΡ
ΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
Π³Π°ΡΠ°Π½ΡΠΈΠΉ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½ΡΡΠΈΡΡΡΠ° Ρ ΡΡΠ΅ΡΠΎΠΌ Π½ΠΎΠ²ΡΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΡ ΠΏΠΎ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π²Π½ΡΡΡΠ΅Π½Π½Π΅Π³ΠΎ Π°ΡΠ΄ΠΈΡΠ° Π² ΠΠ½ΡΡΠΈΡΡΡΠ΅ ΠΏΡΠΈΡΠΎΠ΄Π½ΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ² ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π·Π°ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΡΠΌ, ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡΠΌ ΠΠ‘Π 9001:2008
ΠΠ΅ΠΎΡ ΠΈΠΌΠΈΡΠ΅ΡΠΊΠ°Ρ Π·ΠΎΠ½Π°Π»ΡΠ½ΠΎΡΡΡ ΡΠΊΠ°ΡΠ½ΠΎΠ²ΠΎ-Π·ΠΎΠ»ΠΎΡΠΎΡΡΠ΄Π½ΡΡ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΉ ΠΠ°ΠΏΠ°Π΄Π½ΠΎΠΉ Π‘ΠΈΠ±ΠΈΡΠΈ
ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Π° Π³Π΅ΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠ°Ρ Π·ΠΎΠ½Π°Π»ΡΠ½ΠΎΡΡΡ Π·ΠΎΠ»ΠΎΡΠΎ-ΡΠΊΠ°ΡΠ½ΠΎΠ²ΡΡ
ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΉ ΠΠ°ΠΏΠ°Π΄Π½ΠΎΠΉ Π‘ΠΈΠ±ΠΈΡΠΈ. ΠΡΡΠ²Π»Π΅Π½ΠΎ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·ΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΡΡΡΠΎΠ΅Π½ΠΈΠ΅ Π°Π½ΠΎΠΌΠ°Π»ΡΠ½ΡΡ
ΡΡΡΡΠΊΡΡΡ Π³Π΅ΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ»Π΅ΠΉ, ΡΠΎΠΏΡΠΎΠ²ΠΎΠΆΠ΄Π°ΡΡΠΈΡ
ΠΈΠ·ΡΡΠ΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ. ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ Π³ΡΡΠΏΠΏΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠΈΡΡΡΡΠΈΡ
ΡΡ ΠΈ Π΄Π΅ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠΈΡΡΡΡΠΈΡ
ΡΡ (ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ Π·ΠΎΠ»ΠΎΡΠΎΡΡΠ΄Π½ΡΠΌ ΡΠ΅Π»Π°ΠΌ) ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ². Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π° ΡΠ΅ΡΠ½Π°Ρ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½Π°Ρ ΡΠ²ΡΠ·Ρ Π·ΠΎΠ»ΠΎΡΠ° Ρ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠΌ Ρ
Π°Π»ΡΠΊΠΎΡΠΈΠ»ΡΠ½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²-ΡΠΏΡΡΠ½ΠΈΠΊΠΎΠ², Π½Π°Π±ΠΎΡ ΠΊΠΎΡΠΎΡΡΡ
ΠΌΠΎΠΆΠ΅Ρ ΠΈΠ·ΠΌΠ΅Π½ΡΡΡΡΡ Π² Ρ
ΠΎΠ΄Π΅ ΡΠ²ΠΎΠ»ΡΡΠΈΠΈ Π³ΠΈΠ΄ΡΠΎΡΠ΅ΡΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ. ΠΠ°Π±ΠΎΡ Π΄Π΅ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠΈΡΡΡΡΠΈΡ
ΡΡ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ², Π½Π°ΠΊΠ°ΠΏΠ»ΠΈΠ²Π°ΡΡΠΈΡ
ΡΡ ΠΏΠΎ ΠΏΠ΅ΡΠΈΡΠ΅ΡΠΈΠΈ ΡΡΠ΄Π½ΡΡ
ΡΠ΅Π», Π² ΡΠ΅Π»ΠΎΠΌ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΠΉ ΠΈ Π²ΠΊΠ»ΡΡΠ°Π΅Ρ Π² ΡΠ΅Π±Ρ Ni, Co, Cr, V, Ba, Mn. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π°ΡΠΏΠ΅ΠΊΡΠΎΠ² Π²ΡΡΠ²Π»Π΅Π½Π½ΠΎΠΉ Π³Π΅ΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·ΠΎΠ½Π°Π»ΡΠ½ΠΎΡΡΠΈ
ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅ΡΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ ΡΠ΅ΡΠ΄ΡΠ° ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°
This paper deals with the modeling of the electrical system of the human cardiac tissue. The paperβs 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
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