Browsing by study line "Beräkningsmaterialfysik"

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.

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.

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.

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.

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.

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.

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.

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.

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.