The lack of a decent solid-state ionic conductor has hindered the large-scale application of solid-state batteries, which are considered to be the potential game changer for energy transition. The recently reported K doping CsPbF3 material system has shed light on this problem. This material possesses high ionic conductivity and a wide electrochemical stability window at the same time, making it a highly promising candidate for the next-generation fluoride ion solid-state battery. In order to have a clearer understanding of the structural information of this material and to find out what contributes to the outstanding properties it demonstrates, this thesis project uses Density functional theory(DFT) to calculate its ground state properties. Meanwhile, to better understand its local structure, the Nuclear magnetic resonance(NMR) parameters for this material are also calculated using DFT and analyzed in detail. Results generated from the calculations suggest that the coulombic interaction can be utilized to explain the structural deformation upon doping K into the CsPbF3 system. Additionally, the analysis of the optimized cell structure indicates a tendency for the material system to go through a cubic to tetragonal phase transition, which reproduces the trend observed experimentally and offers a potential explanation for the driving force behind it. Further investigation using Nudged elastic band calculations(NEB) also reveals a relatively low energy barrier for vacancies to diffuse in the crystal structure, which provides insight into the high ionic conductivity of this material. The findings manifested in this thesis project could potentially offer improvement directions for the K-doped CsPbF3 system and contribute to the development of other solid-state ionic conductors.

Quantum computing has gained a lot of interest from researchers and industry due to its great potential to solve some complex problems in various fields. One of the biggest challenges is developing hardware suitable for the extremely low operation temperatures required by quantum computers. Specifically, the wiring material of a quantum computer must provide good electrical conductivity while keeping the thermal load to a minimum. Delft Circuits’ cryogenic flexible cable design made from metalized Polyimide (PI) with Silver (Ag) thin films as conductors offers a promising solution for quantum computer i/o systems. This thesis aims to investigate the effect of fabrication and design parameters on the electrical and thermal properties of silver thin films, utilizing the Residual Resistivity Ratio (RRR) as an indicator to assess the thermal load of cryogenic flexible cables. The RRR is calculated by taking the ratio of electrical resistivity measured at room temperature and extremely low temperatures of liquid He (4.2 K). The key parameters for the experimental investigation include oven heat treatment, lamination, silver purity, film thickness, aging and storage. The RRR behavior of samples with varying fabrication and design characteristics is explored through cryogenic measurements. Subsequently, the RRR results are analyzed and supported by material characterization via SEM and XRD. The results show that heat treatment results in higher RRR values due to grain growth and lower grain boundary density, leading to lower electrical resistivity at low temperatures. Heat treatment parameters including temperature, pressure, duration, ambiance, and cooling rate play a significant role in the resulting microstructure of the silver film, and consequently, in their RRR values. Furthermore, the study reveals that the lower purity level leads to decreased RRR values of the silver film due to higher electron scattering caused by impurities. A linear relationship is found between the film thickness and the RRR behavior of silver thin films. Lastly, aging and storage do not result in a significant change in the RRR values of heat-treated silver thin films. This thesis provides a deeper understanding on the influence of fabrication and design parameters on the low-temperature resistivity of silver thin films. It highlights the key role of these parameters in tailoring the microstructure of silver thin films to achieve desired material properties.

The Pd-based metal membranes have been focused on many studies, because they are the most promising candidates for hydrogen separation. If Pd-based membranes are designed in nanoscale, then the introduced high volume fraction of grain boundaries can act as fast diffusion paths for hydrogen atoms that leading to an increase of hydrogen separation rate. However, due to the substantial amounts of grain boundaries, the nanocrystalline material tends to show a rapid grain growth at operating temperature (that leading to the coarse grains), which may lead to the failure of this membrane in further applications. However, grain growth could be effectively inhibited by thermodynamic stabilization method-that is solute segregation to grain boundary region. In this thesis, a thermodynamic grain boundary segregation model is developed and is applied to many types of metallic binary alloys, including the Pd-Cu, Pd-Zr, Pd-Y, Fe-Zr, Y-Fe and so on. From this model, the segregation tendency of alloying constituents is determined, and the solute concentration in grain boundary is obtained. Moreover, the grain boundary energy (GB) of investigated alloys can be calculated via this model, by comparing the calculated GB energy with that of pure Pd, we are able to determine whether these alloys can be stabilized by the segregation-introduced thermodynamic stabilization. These calculated results have been compared with those reported in the literature, verifying the predictability of this model. In addition, X-ray diffraction (XRD) analysis, including the XRD residual stress measurement and X-ray peak broadening analysis have been performed on both Pd0.7Cu0.3 and pure Pd thin films, the experimental results will help us determine whether the solute segregation occurs and whether this alloy can be stabilized by the solute segregation.

Interest in transition metal surfaces has grown in fields such as catalysts and semiconductors. Models of initial adsorption and growth of adsorbates are used to verify if certain structures are formed. Structural complexity and diversity of overlayers often determines this growth. Understanding the difference between a clean single crystal transition metal surface and adsorbate covered surfaces at the atomic scale are a first step. Characterization at atomic scale however can be challenging and help can come from electronic structure theory. Much is known about the overlayers of oxygen on a Ru(0001) surface, yet little research has been conducted in understanding the interaction between phosphorus and Ru(0001). In this work the adsorption mechanism of phosphorus on a ruthenium (0001) is studied. Density functional theory (DFT) calculations were performed to find optimal adsorption sites on a Ru(0001) surface for P. The four high coordination positions were then used to form distinct combinations upon stacking layers. There it became evident that in the regime of first layers, the adhesion and stacking of adsorbate layers is governed by the Ru(0001) stacking order and highest probable coordination sites on the surface. Upon stacking more layers, the adsorbate layer follows a pattern of ’on top’ stacking with alternating layer heights. The bond distances and lack of electron transfer show a tendency to form P-P bi-layers. Binding enegies within the P-P bilayer (intra-bilayer) are stronger than binding energies between successive bi-layers (inter-bilayers). Latter one being dominated by VanderWaals forces. A minimal analytical sum of binding interactions is proposed to the surface and bilayer energies showing an accurate description of the DFT results. Bilayer formation and weak inter-bilayer interactions indicate the possibility of formation of two-dimensional phosphorene structures. At last as a point for future work possible supercells are constructed an tabulated that could accommodate adhesion of phosphorene (oxide) structures with minimal strain.

Laser surface treatments offer interesting prospects for the creation of architectured microstructures formed by distinct phases in metastable austenitic steels. In the present study, a laser-based localised heat treatment was developed to locally create an austenitic region in a quenched Fe25Ni0.2C martensitic microstructure. The highly localised laser heat flux gives rise to high spatial gradients in peak temperature and heating rate. This results in strong variations in the microstructures observed over short distances, which are related to local changes in the martensite to austenite phase transformation temperatures and formation mechanisms, the occurrence of grain growth and recrystallization in the newly formed austenite, and the tempering of the initial martensite. Moreover, thermal stresses and surface effects influence the final microstructure. In this work, effects of heating rates and peak temperatures are studied by dilatometry whereas Electron Backscatter Diffraction (EBSD) and optical microscopy are used to assess austenite grain size and morphology. This information is linked to a Finite Element Model of the local thermal history to investigate the evolution of the local microstructure throughout the zone affected by the laser heat source. This research provides insight into localised microstructural control of steels with laser surface treatment and provides a thermal model for detailed understanding of the mechanisms controlling the microstructural changes taking place during these treatments.

For the use of screening potential glassmaking recipes the seeding method has been applied to a Molecular Dynamics simulated CaO-SiO₂ system in order to attain the parameters for the Classical Nucleation Theory and construct a Time-Temperature-Transformation (TTT) diagram. Using this TTT diagram, the non-crystallisation temperature and the critical cooling rate of the material was determined, two quantities important for the prevention of crystallisation during the glassmaking process. The implementation of the seeding method on the CaO-SiO₂ system involved creating a novel local bond order based detection method for distinguishing the crystalline structure of Wollastonite-1A from the glass melt. Regrettably this method had less than desirable precision which resulted in results for the nucleation rate that can only be used qualitatively. In contrast the results for the crystalline growth rate can be used quantitatively, while the resulting TTT diagram again can only be used qualitatively.

Algorithmic optimization is a viable tool for solving complex materials engineering issues. In this study, a data-scarce Bayesian optimization model was developed to research the composition of bio-based composites. The proof-of-concept program adjusts the natural materials' weight ratios to optimize towards user-defined mechanical properties. Preliminary results show that the bio-composites proposed by the program had improved properties compared to existing bulk-moulding compounds. However, the algorithm choice is often arbitrary or based on anecdotal evidence. In parallel, this thesis proposed a data-driven framework for general data-scarce optimization problems to adapt the meta-heuristic during optimization. Guided by the 'No Free Lunch' theorem, we verified that the effectiveness over a selection of algorithms is dependent on problem-specific features and convergence. This effectiveness was captured in a unique identifier metric by optimizing a generated training set of optimization problems. The average solution quality was improved by combining several meta-heuristics in series, based on these problem-specifics. During the optimization of problems in the testing set, the same unique identifier was constructed at predefined stages in the optimization process. Subsequently, the problem was classified, and the meta-heuristic was adapted to the best-performing algorithm based on similar training samples. Experiments with various classifiers and a different number of predefined assessment stages were performed. Results show that the data-driven heuristic decision strategy outperformed the individual optimizers on the testing set. Despite the use of binarization techniques, the classification accuracy was heavily influenced by the imbalanced training set. In terms of computational resources, the various adaptions of the data-driven heuristic strategy are 2.5 times faster in runtime compared to the best-performing meta-heuristic Bayesian Optimization. Lastly, the framework was benchmarked against the 'learning to optimize' study and shows excellent performance on the logistic regression problem compared to the autonomous optimizer. In conclusion, it has been shown that even with the limited information of black-box optimization problems, data-driven optimization effectively improves the current standard of materials engineering processes.

Asphalt concrete is one of the most widely used materials in modern road construction. Predicting its functional properties is crucial in the design of new asphalt concrete mixtures. However, current prediction models are limited in accuracy and applicability due to the complex nature of asphalt concrete properties. This thesis researches the use of machine learning algorithms to greatly improve upon existing prediction models. The input is limited to standardized test results in line with Dutch regulations, the output focusses on functional design parameters including stiffness, fatigue resistance, water sensitivity and resistance to permanent deformations. The performance of several machine learning algorithms and the effects of different regression methods are compared. Furthermore, a solution is found for the inverse problem, which allows for greater flexibility when using the models to design new asphalt concrete mixtures. The results show that machine learning algorithms outperform traditional models on accuracy while simplifying the model input parameters. Machine learning algorithms were also able to predict a greater range of output parameters, most of which with a high accuracy. The analysed possibility of modelling asphalt concrete mixtures directly from their desired functional properties is shown to be promising. The proposed machine learning models and their inverse problem counterparts have the potential to greatly improve the accuracy and practical usability of the prediction of asphalt concrete properties, ultimately leading to better mixture design and more durable roadways.

Master thesis about machine learning in materials science

We perform a study on acoustic metasurfaces, aiming to achieve simultaneously low resonance frequencies (below 400 Hz), high attenuation bandwidth (greater than 200 Hz), and high attenuation coefficient magnitudes (above 0.8), while maintaining a surface-like structure. We propose the implementation of geometrical optimization through genetic algorithms, as well as the incorporation of a chamber to induce resonator coupling in a supercell hexagonal Helmholtz resonator metasurface, to achieve the stated objectives simultaneously. Results show that genetic algorithms can effectively increase the attenuation bandwidth while maintaining a moderate attenuation coefficient magnitude. Incorporating a chamber induces resonator coupling, causing frequency locking and pulling phenomena. A narrow chamber can effectively lower the resonance frequencies and enhance the attenuation coefficients at those frequencies, while maintaining a surface-like structure. However, incorporating a chamber may lead to a reduction in bandwidth. By combining the genetic algorithm optimization with chamber integration, we observe a significant reduction in bandwidth narrowness, while the benefits of frequency locking and pulling are maintained. In conclusion, genetic algorithms have the potential to achieve wide attenuation bandwidths, while chamber incorporation holds promise for attaining low resonance frequencies with high attenuation coefficients. Using both methods simultaneously may enable the achievement of all objectives.

Multiscale computational materials science has reached a stage where many complicated phenomena or properties that are of great importance to manufacturing can be predicted or explained. The word “ab initio study” becomes commonplace as the development of density functional theory has enabled the predictions to be independent of experimental data or empirical parameters. For some crucial phenomena, e.g., precipitation processes in multicomponent alloys, however, challenges exist due to the requirement of an accurate and efficient description of both energetics and kinetics of a complex system. In the present thesis, a systematic methodology has been established for predicting the morphology and realistic formation kinetics of precipitates in multicomponent alloys. Aluminum alloys are chosen as prototype applications of the present methodology, because of the well-known strengthening mechanism—age or precipitation hardening which is a typical and important precipitation process utilized in industrial materials. As one of the main computational approaches, cluster expansion technique is applied to study vacancy properties in concentrated Cu-Ni alloys. Diffusion kinetics in dilute Al-Cu alloys including the role of multiple diffusion barriers has been investigated by kinetic Monte Carlo simulations. At finite temperature, electronic entropy contribution to the free energies of the transition metals is also discussed.@en

Quenching and partitioning (Q&P) is a novel processing route that can create microstructures that combine high strength, ductility and easy formability without the excess use of alloying elements by also keeping the production cost low. Therefore, this method is ideal to be applied to steels that are used in the automotive industry. The material is initially annealed at a high temperature to form austenite. Then it is quenched to a temperature below the martensite start temperature to form a controlled fraction of martensite. Second annealing follows, at a lower temperature than the first one, that stabilises the austenitic phase at room temperature. It has been found that the austenite grain size at the end of the first annealing (prior austenite grain size) has a significant impact on the processing method, the final microstructure and consequently on the final properties of the material. In this thesis, phase-field modelling was employed to investigate the role of prior austenite grain size on the quenching and partitioning process. Simulations of the processing were performed for three different prior austenite grain sizes (6, 25 and 67 μm). Microstructures consisting of about 10.5% austenite and 89.5% martensite were created by simulating the first quenching of the Q&P process. Then, the evolution of the microstructure and carbon distribution were investigated for partitioning at 400 ◦C for times up to 4000 s, and quenching to room temperature. The results gave insights into the effect of the prior austenite grain size on the morphology of the microstructure and the kinetics of carbon during partitioning. It was found that a finer microstructure was derived with decreasing PAGS. Moreover, partitioning becomes more efficient because of the shorter distance that carbon has to diffuse, and the even spatial distribution of austenite. Finally, wide grain size distributions of the austenite that is present during partitioning can lead to significant variations in the carbon content among the austenite grains of the final microstructure, especially for short partitioning times. This affects the efficiency of the partitioning process and has an impact on the mechanical stability of the austenite in the final microstructure and consequently on the TRIP phenomenon.

This thesis demonstrates the feasibility of Extreme-ultraviolet (XUV) high-harmonic generation from structured silica, and was performed at the Advanced Research Centre for Nanolithography (ARCNL). The project focuses on High-harmonic generation (HHG) from condensed matter, and further explores the possibility of high-harmonic generation from micro- and nano-structured silica. HHG from solids was discovered less than a decade ago, and it is expected to be a new source for coherent ultrafast pulses, showing potential in many applications such as HHG spectroscopy, imaging and photonic devices. By generating high-harmonics in structured solids, the capability to control HHG properties by engineering the topology of the surface on solids has been demonstrated. Previous research has demonstrated the control of HHG in the visible light regime by generating from structured semiconductors. In this project, we aim to generate high-harmonics in the XUV regime, and control the properties of XUV light. Our work shows the potential of using structured dielectric materials as new XUV optics, and applications on HHG high-resolution lens-less imaging.

Fossil fuels have been the primary source of rising energy requirements for humankind. However, the extensive use of fossil fuels has led to an increase in Earth's surface temperature. To tackle rising energy demands and the increase in Earth's surface temperature, various organizations like Inter-governmental Panel for Climate Change and the European Environmental Agency have suggested the use of renewable energy as an alternative energy supply. E.g., the use of hydrogen as an alternative fuel in transportation will reduce greenhouse gas emissions. Besides, converting CO2 and N2 to fuels and industrial feedstock like CO or NH¬3 can curb the Earth's increasing surface temperature. As a result of this, in this thesis, the catalysts for the synthesis of hydrogen from water-splitting (hydrogen evolution reaction- HER), conversion of CO2 to CO via carbon dioxide reduction reaction (CO2RR), and reduction of N2 from air to NH3 (Nitrogen reduction reaction-N2RR) are studied. The conventional catalysts used for these reactions are Pt for HER, Cu for CO2RR Cu, and Ru for N2RR. Although these catalysts are active and exhibit a high yield of products, they have some disadvantages, such as the long-term availability and cost of Pt and Ru. On the other hand, Cu suffers from the low selectivity for the conversion from CO2 to CO. To overcome these disadvantages; scientists have developed a new kind of catalyst with a higher specific activity, known as the Single-Atom Catalysts (SAC). The SACs use fewer precious elements than the conventional bulk catalysts without compromising the activity. The use of binding energy (EB) as a descriptor for the reactions mentioned has been proven in the literature. Therefore, EB is used in this thesis to predict novel SACs through high-throughput DFT calculations using 3-N doped graphene as the substrate. The descriptor for HER is the EB of H atom, for CO2RR is EB of CO, and that of N2RR is EB of N on the respective catalyst surfaces. These calculated binding energies are compared against the descriptor EB on the conventional catalysts to obtain the novel SACs. With EB as the descriptor, the candidate catalysts for HER are B, Cr, Mn, Fe, Co, Ni, Ge, Ru, In, Sb, La, and Pb. The candidate catalysts for N2RR are Ru, Mo, and Cr. The candidate catalysts for CO2RR are Mg, Al, Ca, Zn and Se,. In addition to this, the charge dissipation of the adsorbent species on the SAC and the effect of atomic size on the EB is studied. It was seen that the computational predictions go hand in hand with the predictions of existing experiments for HER and CO2RR.