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Browsing by study line "Laskennallinen materiaalifysiikka"

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  • Laakso, Jarno (2021)
    Halide perovskites are a promising materials class for solar energy production. The photovoltaic efficiency of halide perovskites is remarkable but their toxicity and instability have prevented commercialization. These problems could be addressed through compositional engineering in the halide perovskite materials space but the number of different materials that would need to be considered is too large for conventional experimental and computational methods. Machine learning can be used to accelerate computations to the level that is required for this task. In this thesis I present a machine learning approach for compositional exploration and apply it to the composite halide perovskite CsPb(Cl, Br)3 . I used data from density functional theory (DFT) calculations to train a machine learning model based on kernel ridge regression with the many-body tensor representation for the atomic structure. The trained model was then applied to predict the decomposition energies of CsPb(Cl, Br)3 materials from their atomic structure. The main part of my work was to derive and implement gradients for the machine learning model to facilitate efficient structure optimization. I tested the machine learning model by comparing its decomposition energy predictions to DFT calculations. The prediction accuracy was under 0.12 meV per atom and the prediction time was five orders of magnitude faster than DFT. I also used the model to optimize CsPb(Cl, Br)3 structures. Reasonable structures were obtained, but the accuracy was qualitative. Analysis on the results of the structural optimizations exposed shortcomings in the approach, providing important insight for future improvements. Overall, this project makes a successful step towards the discovery of novel perovskite materials with designer properties for future solar cell applications.
  • Lindblom, Otto (2020)
    Due to its exceptional thermal properties and irradiation resistance, tungsten is the material of choice for critical plasma-facing components in many leading thermonuclear fusion projects. Owing to the natural retention of hydrogen isotopes in materials such as tungsten, the safety of a fusion device depends heavily on the inventory of radioactive tritium in its plasma-facing components. The proposed methods of tritium removal typically include thermal treatment of massive metal structures for prolonged timescales. A novel way to either shorten the treatment times or lower the required temperatures is based performing the removal under an H-2 atmosphere, effectively exchanging the trapped tritium for non-radioactive protium. In this thesis, we employ molecular dynamics simulations to study the mechanism of hydrogen isotope exchange in vacancy, dislocation and grain boundary type defects in tungsten. By comparing the results to simulations of purely diffusion-based tritium removal methods, we establish that hydrogen isotope exchange indeed facilitates faster removal of tritium for all studied defect types at temperatures of 500 K and above. The fastest removal, when normalising based on the initial occupation of the defect, is shown to occur in vacancies and the slowest in grain boundaries. Through an atom level study of the mechanism, we are able to verify that tritium removal using isotope exchange depends on keeping the defect saturated with hydrogen. This study also works to show that molecular dynamics indeed is a valid tool for studying tritium removal and isotope exchange in general. Using small system sizes and spatially-parallelised simulation tools, we have managed to model isotope exchange for timescales extending from hundreds of nanoseconds up to several microseconds.
  • Flinck, Oliver (2022)
    In this thesis, sputtering of several low- and high-index tungsten surface crystal directions are investigated. The molecular dynamics study is conducted using the primary knock-on atom method, which allows for an equal energy deposition for all surface orientations. The energy is introduced into the system on two different depths, on the surface and on a depth of 1 nm. Additionally to the sputtering yield of each surface orientation, the underlying sputtering process is investigated. Amorphous target materials are often used to compare sputtering yields of polycrystalline materials with simulations. Therefore, an amorphous surface is also investigated to compare it's sputtering yield and process with crystalline surface orientations. When the primary knock-on atom was placed on the surface all surface orientations had a cosine shaped angular distribution with little variation in the sputtering yield for most of the surface orientations. Linear collision sequences were observed to have a large impact on the sputtering yield when the energy was introduced deeper inside the material. In these linear collision sequences the recoils are traveling along the most close packed atom rows in the material. The distance from the origin of the collision cascade to the surface in the direction of the most close packed row is therefore crucial for the sputtering yield of the surface. Surface directions with high angles between this direction and the surface normal hence show a reduction in the sputtering yield. The amorphous material had a little lower sputtering yield than the crystalline materials when the primary knock-on atoms was placed on the surface whereas the difference rose into several orders of magnitude when the energy was given at 1 nm. It is impossible for linear collision sequences to propagate long distances in the amorphous material and therefore the angular distribution in both cases is cosine shaped. The amorphous material has no long range order and was therefore unable to reproduce the linear collision sequences, which are characteristic for the crystalline materials. The difference in the sputtering yield was hence up to several orders of magnitude as a result when the energy was introduced at 1 nm depth.
  • De Meulder (2022)
    Amorphous metal oxides have proven to deform in a plastic manner at microscopic scale. In this study the plastic deformation and elastic properties of amorphous metal oxides are studied at microscopic scale using classical molecular dynamics simulations. Amorphous solids differ from crystalline solids by not having a regular lattice nor long range order. In this study the amorphous materials were created in simulations by melt-quenching. The glass transition temperature (Tg) depends on the material and cooling rate. The effect of cooling rate was studied with aluminiumoxide (Al2O3) by creating a simulation cell of 115 200 atoms and melt-quenching it with cooling rates of 1011 , 1012 and 1013 K/s. It was observed that faster cooling rates yield higher Tg. The Al2O3 was cooled to 300 K and 50 K after which the material was stretched. The stress-strain curve of the material showed that samples with higher Tg deforms in plastic manner with smaller stresses. The system stretched at 50 K had higher ultimate tensile strength than the system stretched at 300 K and thus confirming the hypothesis proposed by Frankberg about activating plastic flow with work. In order to see if the plastic phenomena can be generalized to other amorphous metal oxides the tensile simulation was performed also with a-Ga2O3 by creating a simulation cell of 105 000 atoms, melt-quenching it and then stretching. Due to the lack of parameters for Buckingham potential these parameters were fitted with GULP using the elastic properties and crystalline structure of Ga2O3. The elastic properties of Ga2O3 with the fitted potential parameters agreed very well with the literature values. The elongated a-Ga2O3 behaved in a very similar fashion compared to a-Al2O3 cooled with the same cooling rate. Further work is needed to establish the Buckingham potential parameters of a-Ga2O3 by experimen tal work. The potential can also be developed further by using the elastic constants and structures of amorphous a-Ga2O3 in the fitting process, although the potential shows already very promising results.
  • Toijala, Risto (2019)
    Ion beams have been the subject of significant industry interest since the 1950s. They have gained usage in many fields for their ability to modify material properties in a controlled manner. Most important has been the application to semiconductor devices such as diodes and transistors, where the necessary doping is commonly achieved by irradiation with appropriate ions, allowing the development of the technology that we see in everyday use. With the ongoing transition to ever smaller semiconductor devices, the precision required of the manufacturing process correspondingly increases. A strong suite of modeling tools is therefore needed to advance the understanding and application of ion beam methods. The binary collision approximation (BCA) as a simulation tool was first introduced in the 1950s. It allows the prediction of many radiation-related phenomena for single collision cascades, and has been adopted in many experimental laboratories and industries due to its efficiency. However, it fails to describe chemical and thermodynamic effects, limiting its usefulness where ballistic effects are not a sufficient description. Parallel to BCA, the molecular dynamics (MD) simulation algorithm was developed. It allows a more accurate and precise description of interatomic forces and therefore chemical effects. It is, however, orders of magnitude slower than the BCA method. In this work, a new variant of the MD algorithm is developed to combine the advantages of both the MD and the BCA methods. The activation and deactivation of atoms involved in atomic cascades is introduced as a way to save computational effort, concentrating the performed computations in the region of interest around the cascade and ignoring surrounding equilibrium regions. By combining this algorithm with a speedup scheme limiting the number of necessary relaxation simulations, a speedup of one order of magnitude is reached for covalent materials such as Si and Ge, for which the algorithm was validated. The developed algorithm is used to explain the behavior of Ge nanowires under Xe ion irradiation. The nanowires were shown experimentally to bend towards or away from the ion beam, and computational simulations might help with the understanding of the underlying physical processes. In this thesis, the high-fluence irradiation of a Ge nanowire is simulated and the resulting defect structure analyzed to study the bending, doubling as a second test of the developed algorithm.
  • Väisänen, Vesa (2023)
    The diffusion of a single hydrogen atom through solid tungsten was studied using two separate computational methods: Molecular dynamics simulations and nudged elastic band calculations. Molecular dynamics was used to track the atom’s path in the tungsten lattice in several successive simulations to obtain its mean squared displacement. Results were obtained for temperatures between 400 K and 700 K. These were used to derive a temperature relation for the diffusion coefficient and an estimate for the diffusion energy barrier, which was 0.1985 eV. The nudged elastic band method was used to directly obtain an estimate for the diffusion energy barrier. The same tungsten lattice system as in the molecular dynamics simulations had the hydrogen atom relaxed into an interstitial position and then moved over the energy barrier into an adjacent site. If the tungsten atoms were allowed to relax during the motion, a comparable value of 0.2022 eV was obtained. Further computations were made with a fixed-length tungsten bond next to the hydrogen starting position, yielding energy barriers from 0.1 eV to 1.2 eV depending on the bond length, direction of the diffusion jump and tungsten relaxation. In conclusion, the two different computational methods do give results for the energy barrier that are comparable in magnitude, but further measurement of the tungsten bonds near the hydrogen atom during the molecular dynamics simulation may be needed for a more matching comparison from the nudged elastic band results.
  • Koitermaa, Roni (2022)
    The complex physical mechanisms involved in the formation of vacuum arcs have been of interest for many decades. Vacuum arcs are relevant in many engineering disciplines, but the physics behind them is not yet fully understood. In recent years, there have been many experimental and computational studies focused on understanding aspects of vacuum arcs. This thesis focuses on further development of a simulation model to describe the physical processes starting from electron emission and leading to the formation of an ionized plasma. The FEMOCS code is extended to include plasma simulation based on previous work on ArcPIC. Emission of electrons and heating of the cathode is simulated using the finite element method, while plasma simulation is performed using the particle-in-cell method. We add evaporation of neutral atoms from the cathode, as well as ionization processes for multiple species of ions. Monte Carlo collisions for elastic, Coulomb, impact ionization, charge exchange and recombination collisions between particles are added. Direct field ionization of neutrals is included to account for ionization at high electric fields. A dynamic weighting scheme is described for adjusting superparticle weights during the simulation. Ion bombardment effects such as bombardment heating and sputtering are added to account for additional supply of neutrals resulting from energetic ions accelerated by the electric field. Finally, we add a circuit model for coupling to an external circuit. A static nanotip is simulated with different parameters to study local field thresholds leading to thermal runaway. We find that our simulations agree with experimental results. The most significant interactions contributing to initial formation of vacuum arcs are identified. We find most neutrals are created via evaporation rather than sputtering. The most important collision for plasma formation is impact ionization of neutrals into Cu+ ions, while higher-order ions are found to play a lesser role. Direct field ionization of neutrals is also found to be significant at high fields on the order of 10 GV/m.
  • Paajanen, Santeri (2022)
    NMDA receptors are ionotropic glutamate receptors (iGluRs), tetrameric proteins, mediating synaptic transmission in the brain and the whole nervous system. Together with another type of iGluRs, AMPA receptors, they are considered essential for neuronal plasticity and memory. Understanding their dynamics and different kinetics is vital for studying various neurological diseases. The relatively slow dynamics, where the time scales of related processes range up to hundreds of milliseconds, make studying them with Molecular Dynamics (MD) simulations challenging. We developed the Functional Sampling Tool (FST), a novel method for enhancing the sampling of a function of interest. Compared to existing enhanced sampling schemes it strikes a balance between generality and simplicity, minimising the need of user input, while allowing for maximal customisability. Using FST, we studied two processes of the NMDA receptor. By keeping all four ligands bound we simulated a desensitisation pathway, and by removing all four we simulated an inactivation pathway. The tool sampled both, giving a good distribution between open and closed states. The tool also allowed us to change the function in the middle of sampling. With the new function we were able to produce more data, focusing on a certain value range.
  • Lipsunen, Werner (2023)
    This thesis examines the implementation of general purpose graphics processing unit (GPGPU) acceleration to a non-equilibrium Green’s function (NEGF) equation solver in the context of a computational photoelectrochemical (PEC) cell model. The goal is to find out whether GPGPU acceleration of the NEGF equation solver is a viable option. The current model does not yet have electron-photon scattering, but from the results it is possible to assess the viability of GPGPU acceleration in the case of a complete PEC cell model. The viability of GPGPU acceleration was studied by comparing the performance difference of two graphics processing unit (GPU) solutions against a multi core central processing unit (CPU) solution. The difference between the two GPU solutions was in the used floating-point precision. The GPU solutions would use LU factorization to solve the NEGF equations, and the CPU solution a banded solver (Gauss tridiagonal) provided by Scipy Python package. The performance comparison was done on multiple different GPU and CPU hardware. The electrical transport properties of the PEC cell were modeled by a self-consistent process in which the NEGF and Poisson equations were solved iteratively. The PEC cell was described as a semiconductor device connected with a metal and electrolyte contacts. The device was assumed to be a simple one dimensional tight-binding atom chain made of GaAs, where the transverse modes in the y–z plane are treated with a logarithmic function. The computational model did lack electron-photon scattering, which would be implemented in the future. From the benchmark results, it can be concluded that the GPGPU acceleration via LU factorization is not a viable option in the current code or in the complete model with electron-photon scattering and the assumed approximations. The parallel multi-core CPU code generally outperformed the GPU codes. The key weakness of the GPU code was the usage of LU factorization. Despite of this, there could be an opportunity for GPGPU acceleration if a more complex lattice structure and more exact scattering terms would be used. Also, a GPU accelerated tridiagonal solver could be a possible solution.
  • Kurki, Lauri (2021)
    Atomic force microscopy (AFM) is a widely utilized characterization method capable of capturing atomic level detail in individual organic molecules. However, an AFM image contains relatively little information about the deeper atoms in a molecule and thus interpretation of AFM images of non-planar molecules offers significant challenges for human experts. An end-to-end solution starting from an AFM imaging system ending in an automated image interpreter would be a valuable asset for all research utilizing AFM. Machine learning has become a ubiquitous tool in all areas of science. Artificial neural networks (ANNs), a specific machine learning tool, have also arisen as a popular method many fields including medical imaging, self-driving cars and facial recognition systems. In recent years, progress towards interpreting AFM images from more complicated samples has been made utilizing ANNs. In this thesis, we aim to predict sample structures from AFM images by modeling the molecule as a graph and using a generative model to build the molecular structure atom-by-atom and bond-by-bond. The generative model uses two types of ANNs, a convolutional attention mechanism to process the AFM images and a graph neural network to process the generated molecule. The model is trained and tested using simulated AFM images. The results of the thesis show that the model has the capability to learn even slight details from complicated AFM images, especially when the model only adds a single atom to the molecule. However, there are challenges to overcome in the generative model for it to become a part of a fully capable end-to-end AFM process.
  • Kauppala, Juuso (2021)
    The rapidly increasing global energy demand has led to the necessity of finding sustainable alternatives for energy production. Fusion power is seen as a promising candidate for efficient and environmentally friendly energy production. One of the main challenges in the development of fusion power plants is finding suitable materials for the plasma-facing components in the fusion reactor. The plasma-facing components must endure extreme environments with high heat fluxes and exposure to highly energetic ions and neutral particles. So far the most promising materials for the plasma-facing components are tungsten (W) and tungsten-based alloys. A promising class of materials for the plasma-facing components is high-entropy alloys. Many high-entropy alloys have been shown to exhibit high resistance to radiation and other wanted properties for many industrial and high-energy applications. In materials research, both experimental and computational methods can be used to study the materials’ properties and characteristics. Computational methods can be either quantum mechanical calculations, that produce accurate results while being computationally extremely heavy, or more efficient atomistic simulations such as classical molecular dynamics simulations. In molecular dynamics simulations, interatomic potentials are used to describe the interactions between particles and are often analytical functions that can be fitted to the properties of the material. Instead of fixed functional forms, interatomic potentials based on machine learning methods have also been developed. One such framework is the Gaussian approximation potential, which uses Gaussian process regression to estimate the energies of the simulation system. In this thesis, the current state of fusion reactor development and the research of high-entropy alloys is presented and an overview of the interatomic potentials is given. Gaussian approximation potentials for WMoTa concentrated alloys are developed using different number of sparse training points. A detailed description of the training database is given and the potentials are validated. The developed potentials are shown to give physically reasonable results in terms of certain bulk and surface properties and could be used in atomistic simulations.
  • Muff, Jake (2023)
    Quantum Monte Carlo (QMC) is an accurate but computationally expensive technique for simulating the electronic structure of solids, with its use as a simulation technique for modelling positron states and annihilation in solids relatively new. These simulations can support positron annihilation spectroscopy and help with defect characterisation in solids and vacancy identification by calculating the positron lifetime with increased accuracy and comparing them to experimental results. One method of reducing the computational cost of simulations whilst maintaining chemical accuracy is to employ pseudopotentials. Pseudopotentials are a method to approximate the interactions between the outer valence electrons of an atom and the inner core electrons, which are difficult to model. By replacing the core electrons of an atom with an effective potential, a level of accuracy can be maintained whilst reducing the computational cost. This work extends existing research with a new set of pseudopotentials with fewer core electrons replaced by an effective potential, leading to an increase in the number of core electrons in the simulation. With the inclusion of additional core electrons into the simulation, the corrections that need to be made to the positron lifetime may not be needed. Silicon is chosen as the element under study as the high electron count makes it difficult to model accurately for positron simulations. The suitability of these new pseudopotentials for QMC is shown by calculating the cohesive and relaxation energy with comparisons made to previously used pseudopotentials. The positron lifetime is calculated from QMC simulations and compared against experimental and theoretical values. The simulation method and challenges due to the inclusion of more core electrons are presented and discussed. The results show that these pseudopotentials are suitable for use in QMC studies, including positron lifetime studies. With the inclusion of more core electrons into the simulation a positron lifetime was calculated with similar accuracy to previous studies, without the need for corrections, proving the validity of the pseudopotentials for use in positron studies. The validation of these pseudopotentials enables future theoretical studies to better capture the annihilation characteristics in cases where core electrons are important. In achieving these results, it was found that energy minimisation rather than variance minimisation was needed for optimising the wavefunction with these pseudopotentials.
  • Dursun, Sahin (2021)
    This thesis focuses on the initial measurements and development of a 1.5K target temperature cryostat to contain all quantum standards in the quantum metrological triangle. Currently, the cryostat can reach 4.2K end temperature with a cryocooler to liquefy helium for cooling experiments related to the quantum standards of SI-units which require 1.5K end temperature and the 1.5K cooling circuit is one of the research questions of the thesis for future development of the cryostat. Measurements included cooling cycles in a helium environment for liquefaction, as well as no load conditions. Results of the experimentation conclude that liquefaction was unsuccessful at this stage, but could be achieved with improved temperature control. The thesis is based on experimental work carried out in VTT technical research center of Finland, during the course of which the cryostat was progressively developed towards the future utility of combining experiments that realize the quantum standards of Ampere, voltage and resistance under a single low temperature experimental platform, the unified quantum standard cryostat.
  • Grönroos, Sonja (2021)
    Several nuclear power plants in the European Union are approaching the ends of their originally planned lifetimes. Extensions to the lifetimes are made to secure the supply of nuclear power in the coming decades. To ensure the safe long-term operation of a nuclear power plant, the neutron-induced embrittlement of the reactor pressure vessel (RPV) must be assessed periodically. The embrittlement of RPV steel alloys is determined by measuring the ductile-to-brittle transition temperature (DBTT) and upper-shelf energy (USE) of the material. Traditionally, a destructive Charpy impact test is used to determine the DBTT and USE. This thesis contributes to the NOMAD project. The goal of the NOMAD project is to develop a tool that uses nondestructively measured parameters to estimate the DBTT and USE of RPV steel alloys. The NOMAD Database combines data measured using six nondestructive methods with destructively measured DBTT and USE data. Several non-irradiated and irradiated samples made out of four different steel alloys have been measured. As nondestructively measured parameters do not directly describe material embrittlement, their relationship with the DBTT and USE needs to be determined. A machine learning regression algorithm can be used to build a model that describes the relationship. In this thesis, six models are built using six different algorithms, and their use is studied in predicting the DBTT and USE based on the nondestructively measured parameters in the NOMAD Database. The models estimate the embrittlement with sufficient accuracy. All models predict the DBTT and USE based on unseen input data with mean absolute errors of approximately 20 °C and 10 J, respectively. Two of the models can be used to evaluate the importance of the nondestructively measured parameters. In the future, machine learning algorithms could be used to build a tool that uses nondestructively measured parameters to estimate the neutron-induced embrittlement of RPVs on site.
  • He, Ru (2023)
    Ga2O3 has been found to exhibit excellent radiation hardness properties, making it an ideal candidate for use in a variety of applications that involve exposure to ionizing radiation, such as in space exploration, nuclear power generation, and medical imaging. Understanding the behaviour of Ga2O3 under irradiation is therefore crucial for optimizing its performance in these applications and ensuring their safe and efficient operation. There are five commonly identified polymorphs of Ga2O3 , namely, β, α, γ, δ and structures, among these phases, β-Ga2O3 is the most stable crystal structure and has attracted majority of the recent attention. In this thesis, we used molecular dynamic simulations with the newly developed machine learned Gaussian approximation potentials to investigate the radiation damage in β-Ga2O3 . We inspected the gradual structural change in β-Ga2O3 lattice with increase doses of Frenkel pairs implantations. The results revealed that O-Frenkel pairs have a strong tendency to recombine and return to their original sublattice sites. When Ga- and O-Frenkel pairs are implanted to the same cell, the crystal structure was damaged and converted to an amorphous phase at low doses. However, the accumulation of pure Ga-Frenkel pairs in the simulation cells might induce a transition of β to γ-Ga, while O sublattice remains FCC crystal structure, which theoretically demonstrated the recent experiments finding that β- Ga2O3 transfers to the γ phase following ion implantation. To gain a better understanding of the natural behaviour of β-Ga2O3 under irradiation, we utilized collision cascade simulations. The results revealed that O sublattice in the β-Ga2O3 lattice is robust and less susceptible to damage, despite O atoms having higher mobility. The collision and recrystallization process resulted in a greater accumulation of Ga defects than O defects, regardless of PKA atom type. These further revealed that displaced Ga ion hard to recombine to β- Ga lattice, while the FCC stacking of the O sublattice has very strong tendency to recovery. Our theoretical models on the radiation damage of β-Ga2O3 provide insight into the mechanisms underlying defect generation and recovery during experiment ion implantation, which has significant implications for improving Ga2O3 radiation tolerance, as well as optimizing its electronic and optical properties.
  • Djurabekova, Amina (2022)
    Energy is an essential input for any non-spontaneous mechanism. In biological organisms, the process of producing energy currency, adenosine triphosphate, is called cellular respiration. It is made of three smaller steps, out of which the last one is oxidative phosphorylation that is responsible for the largest production of adenosine triphosphate molecules in the whole process. Oxidative phosphorylation is performed by the electron transport chain made of five protein complexes, named respiratory complex I-V. Complex I is the first and largest protein complex in the electron transport chain, and it is the least understood. Its primary function is to transfer electrons from nicotinamide adenine dinucleotide to ubiquinone, which is coupled to the pumping of four protons across the mitochondrial inner membrane. Although the overall reaction of complex I is understood, the intricate detail of the mechanism is still largely unknown. There is significance in the details because there are numerous point mutations, which have been strongly correlated with neurogenerative diseases, such as Leigh’s syndrome, and aging. Therefore, a more thorough understanding of its mechanism can give insight into potential target drug development. Complex I is made of 14 highly conserved subunits that can be found in most species that use the electron transport chain. They create an L-shape, where seven subunits are embedded in the inner membrane, the membrane domain, and the others are floating in the mitochondrial matrix, the peripheral arm. In mitochondrial complex I, however, there are in addition around 30 accessory subunits. It has been previously thought that the main mechanism is conducted by the 14 subunits that are found in all species. However, in the past couple of years, it has been shown that accessory subunits can play an important role in the mechanism of mitochondrial complex I. The work presented in this thesis uses a multiscale computational approach to study the effect of three mutations, F89A, Y43A and L42A, from an accessory subunit LYRM6 on the function of complex I. Previous experiments demonstrated that the mutations decreased the overall activity of complex I by 76-86 %. The LYRM6 subunit is located at the pivot of the membrane and periplasmic domains. The results of this study show that the point mutations have a long-range effect on the conformations of three loops from three conserved subunits in this region. The shift in the loop dynamics causes a drop in water occupancy. The observed water pathway is tested for the capability of proton transfer. The findings are demonstrated with the help of molecular dynamics and quantum mechanics/molecular mechanics simulations.
  • Kivelä, Feliks (2022)
    The crystal structure of magnetite (Fe3O4) involves Fe2+ ions in sites with octahedral (Oh) symmetry and Fe2+ and Fe3+ ions in sites with tetrahedral (Td) symmetry. Magnetite exhibits several interesting physical phenomena, such as the Verwey transition, in which the roles of the different Fe sites are an active subject of research. In the X-ray standing wave (XSW) technique, incoming and diffracted X-ray beams interfere inside a crystal, creating a standing wave with the periodicity of the diffracting atomic lattice. The phase of the wave, i.e. whether the nodes are located on the lattice planes or between them, can be adjusted by finely tuning the diffraction angle. Changing the phase in this way makes it possible to selectively vary the contributions of different atoms and absorption types (dipole versus quadrupole) to the measured total absorption spectrum. Iron K-edge absorption spectra in magnetite were studied in the presence of an XSW in an experiment conducted at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. This thesis presents an analysis of the data gathered during the experiment, with the goal of decomposing the experimentally measured pre-edge peak into its constituent components. The methods used in the analysis include principal component analysis and fitting predicted absorption peaks calculated with the Quanty software to the experimental data. The results show the dipole and quadrupole contributions of the tetrahedral sites responding to changes in the phase of the XSW in opposite ways in a manner consistent with theoretical predictions.
  • Fellman, Aslak (2021)
    The plasma-facing materials of future fusion reactors are exposed to high doses of radiation. The characterization of the radiation damage is an essential part in the study of fusion relevant materi- als. Electron microscopy is one of the most important tools used for characterization of radiation damage, as it provides direct observations of the microstructure of materials. However, the char- acterization of defects from electron microscope images remains difficult. Simulated images can be used to bridge the gap between experimental results and models. In this thesis, scanning transmission electron microscope (STEM) images of radiation damage were simulated. Molecular dynamics simulations were employed in order to create defects in tungsten. STEM images were simulated based on the created systems using the multislice method. A data- base of images of h001i dislocation loops and defects produced from collision cascade simulations was generated. The simulated images provide insight into the observed contrast of the defect structures. Differences in the image contrast between vacancy and interstitial h001i dislocation loops were reported. In addition to this, the results were compared against experimental images and used in identification of a dislocation loop. The simulated images demonstrate that it is feasible to simulate STEM images of radiation damage produced from collision cascade simulations.
  • Koskenniemi, Mikko (2023)
    High-entropy alloys (HEAs), esteemed for their exceptional resistance to radiation damage, carry considerable potential for deployment within fusion reactors. Nonetheless, due to their compositional complexity, comprehending the diffusion behaviour in these multifaceted alloys continues to be a daunting task. This thesis proposes a novel approach to modelling vacancy diffusion in body-centred cubic (BCC) HEAs, particularly Mo-Nb-Ta-V-W. The methodology involves the tactical application of collective variable-driven hyperdynamics (CVHD) to procure data for training a Gaussian process regression (GPR) and feed-forward neural network (FNN) model. The trained FNN model is subsequently employed within kinetic Monte Carlo (KMC) simulations for accurately predicting jump rates, whereas the GPR model is used to elucidate experimental findings related to the behaviour of vacancies in \mbox{Mo-Nb-Ta-V-W}. The robustness of the FNN model is manifested by its capacity to generalise to unseen data, whilst the efficacy of the overall method is corroborated by Monte Carlo molecular dynamics (MCMD) simulations. The CVHD methodology, uniquely capable of functioning at finite temperatures, can capture the entropic contribution to the free energy and model kinematics explicitly. This singular ability facilitates a more comprehensive understanding of the system's behaviour under authentic conditions. The findings presented in this thesis signify a considerable stride forward in the study of HEAs, providing a robust framework for the design of advanced materials. These results underscore the potential of the CVHD-trained FNN-KMC methodology in exploring complex environments, thereby establishing a firm foundation for future investigations and emphasising the need for its continued refinement and augmentation.