Deformation is a key element in most thermomechanical processing routes for steel; therefore, understanding how deformation affects phase transformation kinetics is essential for effective microstructure control and for tailoring mechanical properties. In this study, different levels of compressive strain (ε2 = 0.0, 0.3 and 0.5), followed by continuous cooling at different rates (1, 10, 50 and >100 K/s), were applied to a Nb-microalloyed low-carbon steel (0.05C-1.3Mn-0.042Nb-0.2Si-0.010Ti-0.03Al-0.0040N-Bal. Fe, wt.%). To quantify the influence of deformation in the austenite on the transformed product, the resulting microstructure, grain size, grain boundary character, and hardness were analyzed and the combined results were used to construct a continuous cooling transformation (CCT) diagram.

Results show that with increasing deformation, the pearlite fraction of the slow-cooled samples decreases. Samples with higher cooling rates and increasing deformation exhibit increased variability in the resulting microstructures, due to shifts in the CCT curves. The results show that increasing deformation in the austenite generally leads to grain refinement in the transformed phases for all cooling rates. However, despite the reduction in grain size with increasing deformation, the hardness decreases with increasing deformation for all cooling conditions. For the slow-cooled samples, it is associated with a decrease in the pearlite fraction. However, for the samples processed at high cooling rates, this behavior is explained by the nature of the microstructural constituents present in the samples, which, they evolve from martensite to bainite and eventually to ferrite with increasing deformation. Grain boundary analysis reveals that the effect of deformation on total grain boundary length strongly depends on the cooling rate. At low cooling rates, deformation promotes the formation of low-angle grain boundaries (LAGBs), and at high cooling rates, the grain boundary length decreases with increasing deformation, attributed to changes in the resulting microstructures.

In the CCT diagram, deformation shifts the ferrite transformation curves to shorter times and locally to higher temperatures, while the bainite curves are also shifted to shorter times, but locally to lower temperatures. The martensitic transformation is suppressed to a lower temperature with increasing deformation. Overall, the results demonstrate that prior deformation has a pronounced effect on the microstructural evolution and phase transformation kinetics of a Nb-microalloyed low-carbon steel.

Hydrogen is a promising energy carrier for sustainable energy systems, but its interaction with metallic structures poses significant challenges, particularly hydrogen embrittlement. Multi-principal element alloys (MPEAs), including high- and medium-entropy alloys, offer resistance to hydrogen embrittlement and potential for hydrogen storage due to their disordered atomic lattices, which create effective trapping sites for hydrogen. However, the vast compositional space of MPEAs limits experimental exploration, and conventional simulation approaches are often too computationally intensive for high-throughput screening. This thesis introduces an efficient and comprehensive computational framework for predicting hydrogen diffusivity in body-centred cubic (BCC) MPEAs. The diffusion energy landscape is characterised by statistical parameters that describe the distribution of saddle point and well-energies. Through kinetic Monte Carlo (KMC) simulations, a large dataset of hydrogen diffusivity was generated using synthetic energy landscapes defined by these statistical parameters. Machine learning symbolic regression (MLSR) was then employed to derive analytical expressions that relate the statistical descriptors to macroscopic diffusivity. To apply the model to real alloys, hydrogen diffusivity is obtained through the MLSR expressions based on energy landscape statistics that were determined using climbing-image nudged elastic band (CI-NEB) calculations with universal machine learning interatomic potentials (uMLIPs). The predictions were validated against molecular dynamics (MD) simulations, showing reasonable agreement. This framework enables fast, scalable prediction of hydrogen diffusion in complex alloys, supporting accelerated materials discovery for hydrogen-related applications.

The detrimental effects of Cu contamination during steel recycling and production are primarily due to the segregation of Cu at Fe grain boundaries (GBs). A promising approach to mitigate these effects is the introduction of alloying elements that inhibit Cu segregation at Fe GBs, which can be investigated through atomistic simulations. Currently, popular simulation methods include density functional theory (DFT)-based simulations and empirical interatomic potentials (EIPs)-based simulations. DFT calculations can provide simulations with high accuracy, while the high computational cost limits their application to simpler GB structures. Moreover, simulations utilizing EIPs may lack reliable potentials, especially when predicting GB segregation tendencies across a broader range of alloy systems. To address these challenges, universal machine learning interatomic potentials (uMLIPs), which are trained on DFT data and applicable for most of elements, have emerged as a promising alternative. Although uMLIPs have shown potential in various materials simulation tasks, their reliability for out-of-distribution tasks, such as simulating GB segregation behavior, remains unproven.

This thesis project evaluates the performance of the best available uMLIPs, specifically MACE-MP-0, CHGNet, M3GNet, and SevenNet-0, in predicting single-solute GB segregation energies, GB energies for both body-centered cubic (BCC) Fe and face-centered cubic (FCC) Fe systems, and solution enthalpies for BCC Fe and cementite. The results were compared against existing studies conducted via DFT calculations to assess the accuracy and applicability of each uMLIP. Additionally, some EIPs were tested for comparison, serving as an extra reference. The findings reveal that MACE-MP-0 generally outperforms the other uMLIPs in both accuracy and stability of convergence. While all tested uMLIPs perform well in BCC Fe systems, CHGNet(v0.2.0) and SevenNet-0 show reduced accuracy in FCC Fe simulations. Although most of the simulations using uMLIPs converged well in BCC Fe GBs, many unconverged cases were reported in FCC Fe systems, particularly for uMLIPs other than MACE-MP-0 and CHGNet(v0.3.0). Furthermore, a consistent underprediction of segregation tendencies for highly segregating solute elements, such as Cu, is observed in the results of MACE-MP-0 and CHGNet. This suggests that while uMLIPs hold significant potential for atomistic simulations, fine-tuning pre-trained uMLIP models for out-of-distribution tasks, such as calculating GB segregation energy, is recommended to improve accuracy and convergence behavior. This work offers a valuable benchmark for using uMLIPs in future GB segregation studies.

Pursuing sustainable and efficient energy storage technologies has led to advancements in iron-air batteries. Understanding the intricate relationship between the microstructural features of iron electrodes and their oxidation and reduction behaviour is crucial for optimizing battery performance and lifespan. This thesis aims to investigate the impact of microstructural characteristics, such as phases, grain size, and defect density, on the formation of stable iron oxide/hydroxide compounds and the evolution of hydrogen gas (HER), using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) experiments in 6M KOH. The influence of an electrolyte additive, sodium stannate trihydrate, and an iron foam functioning as alternative electrode material are also examined.
Hot-rolled, pure iron samples were subjected to annealing heat treatments, resulting in different grain-sized specimens. A dual-phase steel, DP1000 steel composed of ferrite and martensite phases as well as hot-rolled and cold-rolled iron electrodes, completed the materials that formed this study’s basis. Initial surface identification via optical microscopy (OM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), atomic force microscopy (AFM) and X-ray diffraction (XRD) have been performed.
Results indicate a relatively lower formation of FeOOH for a DP1000 steel anode compared to cold-rolled alpha iron over 12 cycles. A marginally larger decline in HER kinetics is observed for a hot-rolled small grained anode compared to a coarse grained anode, while a clear effect of grain size on the development of Fe₃O₄ and FeOOH could not be established. An iron foam electrode showcases greatly enhanced anodic and cathodic current densities in comparison to solid sheet iron electrodes, due to its cellular structure. The effect of 0.01M sodium stannate added to the electrolyte illustrates a significant reduction in HER intensity for both foam and solid iron samples.

Master thesis about machine learning in materials science

A long standing research field in material science has been the trade-off between strength and ductility of steels. Advanced High Strength Steels (AHSS) aim to combine these two properties and provide steels that can increase safety and lower CO2 consumption in automotive applications, by decreasing weight. To further decrease the environmental impact, novel quenched and partioned (Q&P) martensitic stainless steel is considered to prevent corrosion. Quenching and partitioning is a heat treatment that aims for a martensite/retained austenite microstructure with specific phase fractions to optimize strength and ductility. While mechanical properties are well researched for Q&P treated martensitic stainless steel, the corrosion properties are not that widely documented. This thesis aims to discover the influence of the microstructural development in terms of phase fractions due to the Q&P treatment, and the manganese content on the corrosion properties of martensitic stainless steel. Two alloys with chemical composition 0.2C-12.5Cr-0.35Si-XMn, where X stands for 0.7 for the low manganese alloy, and 3 for high manganese alloy, are considered for this study, and are compared to a commercial AISI 420 martensitic stainless steel. The microstructure was investigated by the use of optical microscopy, energy dispersive X-ray spectroscopy (EDS), electron probe microanalysis (EPMA), scanning Kelvin probe force microscopy (SKPFM). This was combined with electrochemical experiments including Open-circuit potential measurements, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). When compared to commercially available AISI 420 martensitic stainless steel, the novel Q&P MSS showed improved corrosion properties. This can mainly be attributed to the lack of chromium rich carbides (Cr23C6 or Cr7C3) in the Q&P treated MSS. However, this improvement in corrosion properties was only observed for the samples with low manganese content. The increase of fraction retained austenite did show an increase in corrosion potential, making the steel more cathodic, but the improvements in terms of pitting potential and passive film properties are limited. Samples with high fresh martensite fractions also showed an increase in corrosion potential, but only marginally increased in terms of length of the passive region. EIS data shows that the addition of fresh martensite reduces the passivity properties of the material. Volta-potential measurements done by SKPFM showed a clear difference between primary martensite and fresh martensite of up to 20mV and were overlapping with the topography maps. This can be an indication of increased tendency to form micro-galvanic cells between martensite phases. Manganese was found to be detrimental for the corrosion properties of the Q&P treated MSS. A clear drop in the length of the passive region shows that the high manganese samples are more susceptible to pitting. EDS measurements and EPMA elemental distribution maps showed local zones of decreased chromium and increased manganese and silicon. This indicates that there are secondary phase particles present in the material in the form of manganese silicide or manganese sulfide. Volta-potential measurements done by SKPFM showed these particles behave more anodic in comparison to the matrix of the material. The number of particles per unit area was increased for the samples with high manganese content. These inclusions greatly reduce corrosion performance, showing that the addition of manganese is detrimental for corrosion performance. Several other microstructural features are discussed in terms of influence on the corrosion performance of the material. Which include prior austenite grain boundaries, elemental banding of chromium and manganese and Volta-potential differences between martensite laths.