Li-ion batteries have enabled the widespread adoption of portable electronics and are becoming the technology of choice for electric vehicles and grid storage. One of the most promising ways to accommodate this demand is to increase the energy density and cycle life of battery electrode materials. Key strategies promoted in the literature include the use of nickel-rich cathodes as well as high-capacity anodes like silicon and lithium metal. While these materials enable a high energy density, this advantage is often counterbalanced with deficits such as poor stability and high cost. Multicomponent electrodes refer to strategies that try to leverage the relative advantages of different materials to offer an attractive compromise of energy density, cost, and cycle life. This thesis has investigated various aspects of multicomponent electrodes with a special emphasis on silicon-based anodes and high-entropy materials. Silicon (Si) is the second-most abundant element on earth and has one of the highest gravimetric capacities. However, silicon anodes are notorious for their poor cycle stability. Herein, improvements in the stability of silicon-based electrodes are achieved with multicomponent composite strategies involving the use of nanostructured spherical silicon. The nanosilicon is studied in high-fraction (80 wt% Si) and low-fraction (≤20 wt% Si) formulations to investigate both failure mechanisms and practical capacity retention, respectively. Composite strategies in which nanosilicon is encapsulated within a Li₄Ti₅O₁₂ ceramic or MOF-derived carbon matrix are shown to deliver superior capacity retention compared to simple composites of silicon and graphite. Considerable attention is given to the selection of a water-soluble binder and its role in electrochemical stability and electrode cohesion in high-loading silicon electrodes. It is found that unmodified high-molecular-weight sodium carboxymethyl cellulose offers better capacity retention compared to xanthan gum or low-molecular-weight binders. The high-entropy design strategy has created a diverse and largely unexplored set of multicomponent oxides and alloys with great potential as electrode materials. This strategy is applied to the family of layered cathodes, where the synthesis and electrochemical properties of the best-performing Li(NiCoMnTiFe)₁O₂ are reported. Despite the low Ni content, the cathode delivers a high initial capacity with unique overlithiation stability despite being charged to 4.4 V. Throughout the thesis, Operando XRD is used to reveal important insight into the lithiation mechanisms of the multicomponent electrodes including intercalation-based graphite, alloying-based silicon, and a novel organic azaacene.

The application of the entropy concept to stabilize oxide systems opens the possibility of discovering new materials with unique structural and functional properties. High-entropy alloys and oxides, which are based on the entropy stabilization concept and composed of multi-principal elements, have the potential to tailor structural and functional properties to meet specific needs. The study of lanthanum-based perovskite materials that benefit from the entropy stabilization approach is a promising area of research.However, the inherent randomness of multi-principal elements presents new challenges, making it difficult to predict their behavior. To understand these difficulties, we have initiated a methodical investigation of La-based medium- and high-entropy perovskite oxides. This study focuses on the synthesis, characterization, sintering mechanism, and electrical conductivity properties of nine La1-xCax(A1/3, B1/3, C1/3)O3 medium-entropy perovskite oxide systems (A, B, and C = three combination of Cr or Co or Fe or Ni or Mn) and one La1-xCax(Cr0.2Co0.2Fe0.2Ni0.2Mn0.2)O3 high-entropy perovskite oxide system (for x = 0.1 to 0.3). This research aims to provide better understanding of: (1) synthesis process, (2) temperature of single-phase formation, (3) the impact of various combinations of multiple B-site transitional elements and Ca doping on crystal structure, and microstructure (4) sintering mechanism and (5) electrical conductivity properties.

Planar defects such as free surfaces and grain boundaries in metals and alloys play important roles affecting many material properties such as fracture toughness, corrosion resistance, wetting, and catalysis. Their interactions with point defects and solute elements also play critical roles on governing the microstructural evolution and associated property changes in materials. This work seeks to use atomistic modeling to obtain a fundamental understanding of many surface and interface related properties and phenomena, namely: orientation-dependent surface energy of elemental metals and alloys, segregation of solute elements at grain boundaries and their impact on grain boundary cohesive strength, and the controversial sluggish diffusion in both the bulk and grain boundaries of high entropy alloys. First, an analytical formula is derived, which can predict the surface energy of any arbitrary (h k l) crystallographic orientation in both body-centered-cubic (BCC) and face-centered-cubic (FCC) pure metals, using only two or three low-index (e.g., (100), (110), (111)) surface energies as input. This analytical formula is validated against 4357 independent single element surface energies reported in literature or calculated by the present author, and it proves to be highly accurate but easy to use. This formula is then expanded to include the simple-cubic (SC) structure and tested against 4542 surface energies of metallic alloys of different cubic structures, and good agreement is achieved for most cases. Second, the effect of segregation of substitutional solute elements on grain boundary cohesive strength in BCC Fe is studied. It is found that the bulk substitution energy can be used as an effective indicator to predict the embrittlement or strengthening potency induced by the solute segregation at grain boundaries. Third, the controversial vacancy-mediated sluggish diffusion in an equiatomic FeNiCrCoCu FCC high entropy alloy is studied. Many literature studies have postulated that the compositional complexity in high entropy alloys could lead to sluggish diffusion. To test this hypothesis, this work compares the vacancy-mediated self-diffusion in this model high entropy alloy with a hypothetical single-element material (called average-atom material) that has similar average properties as the high entropy alloy but without the compositional complexity. The results show that the self-diffusivities in the two bulk systems are very similar, suggesting that the compositional complexity in the high entropy alloy may not be sufficient to induce sluggish diffusion in bulk high entropy alloys. Based on the knowledge learned from the bulk alloy, the exploration of the possible sluggish diffusion has been extended to grain boundaries, using a similar approach as in the study of self-diffusion in bulk. Interestingly, the results show that sluggish diffusion is evident at a Σ5(210) grain boundary in the high entropy alloy due to the compositional complexity, especially in the low temperature regime, which is different from the bulk diffusion. The underlying mechanisms for the sluggish diffusion at this grain boundary is discussed. / Doctor of Philosophy / Human beings have utilized metals and alloys for over ten millennia and learned much from them. Based on the accumulated knowledge, they have countless applications in our current daily life. However, there is still much to learn for improving our current technology and even opening new opportunities. Throughout most of history, our understanding of these materials was largely obtained through empirical experimentation and refining them into theories and scientific laws. Nowadays, due to the advancements in computer simulations, we can learn more by modeling the behaviors of metals and alloys at the length and time scales that are either be too arduous, costly, or currently impossible experimentally. This work aims at using computer modeling to study some important surface/interface related physical behaviors and properties in metals and alloys at the atomistic scale. First, this work intends to develop a robust surface energy model in an analytical form for any crystallographic orientation. Surface energy is an important material property for many surface-related processes such as fracturing, wetting, sintering, catalysis, and crystalline particle shape. Surface energy is different at different surface orientations, and predicting this difference is important for understanding these surface phenomena. Second, the effect of solute segregation on grain boundary cohesive strength is studied. Most commonly used metallic materials consist of many small crystalline grains and the borders between them are called grain boundaries, which are weak spots for fracture. The minimum energy required to split a boundary is called the grain boundary cohesive strength. The presence of solutes or impurities at grain boundaries can further alter the cohesive strength. A better understanding of this phenomena will eventually help us develop more fracture-resistant materials. The third project deals with the possible sluggish/retarded diffusion in high entropy alloys, which contain five or more principal alloying elements and have many unique mechanical, radiation-resistant, and corrosion-resistant properties. Many researchers attribute these unique properties to the slow species diffusion in these alloys, but its existence is still controversial. This work studies the atomic-level diffusion mechanisms in an FeNiCrCoCu high entropy alloy both in bulk (grain interior) and at grain boundaries in order to determine if sluggish diffusion is present and its causes.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23157号 / 工博第4801号 / 新制||工||1751(附属図書館) / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 辻 伸泰, 教授 乾 晴行, 教授 安田 秀幸 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This dissertation investigates the complex defect diffusion behaviors in concentrated solid solution alloys (CSAs) including high-entropy alloys (HEAs), which are critical for understanding their exceptional mechanical and radiation-resistant properties. Through a combination of multiple atomistic-level simulation techniques and novel machine learning methods, this work reveals how the intricacies of local atomic arrangements and chemical heterogeneities influence diffusion processes, thereby offering new insights into alloy design and optimization. The research initially focuses on the vacancy-mediated diffusion employing binary Ni-Fe concentrated alloys as model systems. To evaluate the impact of local chemical short-range orders (SROs) on vacancy diffusion, both random solid solution configurations and alloys with SROs are prepared using hybrid molecular dynamics (MD) and metropolis Monte Carlo (MMC) methods. The results demonstrates that the development of SROs can significantly impede vacancy-mediated diffusion and enhance the chemically biased diffusion between Fe and Ni sites. Such findings suggest that the diffusion behavior in CSAs can be intricately controlled by adjusting the chemical ordering, a principle that could revolutionize alloy design strategies. Moreover, the study establishes a linear correlation between changes in the enthalpy of mixing and the formation of SROs, indicating that the reduction of enthalpy of mixing towards the more negative direction within an alloy system acts as a driving force for the observed diffusional slowdown. Advancing the methodological frontier, this dissertation introduces a state-of-the-art approach that integrates machine learning (ML) with kinetic Monte Carlo (KMC) simulations to efficiently investigate the controversial phenomenon of "sluggish diffusion" in concentrated alloys. As the first step, the Ni-Fe concentrated alloys are used as model systems. The complexity of defect diffusion in varying local atomic environment in CSAs makes it impractical to apply the standard nudged elastic band (NEB) method for on-the-fly determination of defect migration barriers at each step. By developing an artificial neural network (ANN) model trained on a dataset of NEB-computed migration barriers, it enables precise, efficient, and on-the-fly predictions of vacancy migration barriers for arbitrary local atomic environments during KMC simulations, including both random solution configuration and alloys with SROs. The diffusivities derived from this ANN-KMC modeling closely align with those from independent MD and temperature-accelerated dynamics (TAD) simulations at their accessible temperatures. The research delves into the sluggish diffusion mechanisms over the entire composition range of the Ni-Fe alloy system, elucidating them through the lens of ANN-KMC-derived insights at both high and low temperatures. The exploration then extends to quinary FeNiCrCoCu HEAs, utilizing a similar but improved ANN model to predict vacancy migration barriers across a wide compositional range. Due to the challenges of exploring the vast HEA compositional space, to date most experimental and computational studies have been limited to equiatomic compositions. This model, remarkably effective despite being trained solely on equiatomic HEA data, accurately predicts vacancy migration barriers in non-equiatomic compositions and their binary to quinary subsystems. Implementing this ANN model as an on-the-fly barrier calculator for KMC simulations, such ANN-KMC framework derives diffusivities nearly identical to the those from independent MD simulations but with far higher efficiency. This capability facilitates an extensive study of over 1,500 HEA compositions, uncovering the presence of sluggish diffusion in many non-equiatomic compositions. The analysis provides critical insights into the interplay between compositions, complex potential energy landscape, and percolation effect of the faster diffuser (i.e., Cu) on sluggish diffusion behaviors, offering invaluable perspectives for experimental alloy design and development. Lastly, the dissertation delves into interstitial-mediated diffusion in FeNiCrCoCu HEAs, confirming the presence of sluggish interstitial diffusion by comparing the equiatomic HEA with a range of reference systems. To study the non-monotonic concentration dependences in interstitial diffusion, a machine learning KMC (ML-KMC) method has been developed to simulate 〈100〉 dumbbell interstitial diffusion across various HEA compositions. Diverging from conventional KMC (C-KMC) and random sample KMC (RS-KMC) approaches, which approximate transition energies through a mean-field and random sampling methods, respectively, the ML-KMC predicts dumbbell formation energy on-the-fly based on local atomic configurations. This enables it to effectively replicate diffusion patterns from independent MD simulations. This novel ML-KMC approach offers a promising high-throughput method for studying HEAs, avoiding the expensive computational overhead associated with calculating dumbbell migration barriers. The impact of the percolation effect of faster diffusing elements (Cr, Cu) is also analyzed. Insights from this study can advance the understanding of compositional-dependent diffusion and provide valuable insights for the HEA design. Beyond the achievement of these completed works, two promising future projects have been evaluated that could significantly advance the field of diffusion research. The first initiative seeks to broaden the scope of the ANN-KMC framework, aiming to significantly enhance simulation efficiency across a broad range of HEA compositions. An accurate ANN model for predicting interstitial migration barriers has already been developed, and its full integration into the KMC framework could enable more accurate diffusion simulations. The second project aims to develop a comprehensive ML interatomic potential tailored specifically for HEAs, intended to improve the predictive accuracy of MD simulations. Although progress has been made in modeling an equiatomic CoCrFeMnNi HEA, constructing a robust ML potential for HEAs faces substantial challenges, primarily due to the extensive data requirements and computational demands. / Doctor of Philosophy / This dissertation investigates the complex defect diffusion behaviors in concentrated solid solution alloys (CSAs) including high-entropy alloys (HEAs), which are critical to understanding their exceptional mechanical and radiation-resistant properties. Through a combination of multiple simulation techniques and novel machine learning methods, this work reveals how the intricacies of local atomic arrangements and chemical heterogeneities influence diffusion processes, thereby offering new insights into alloy design and optimization. The research initially focuses on the complex vacancy diffusion mechanism in concentrated Ni-Fe alloys, demonstrating that local chemical short-range orders (SROs) significantly impede vacancy-mediated diffusion. Such findings suggest that the diffusion behavior in CSAs can be intricately controlled by adjusting the chemical ordering, a principle that could revolutionize alloy design strategies. Moreover, the study revealed a linear correlation between changes in the enthalpy of mixing and the formation of SROs, indicating that the enthalpy of mixing may be important for the diffusional behavior in CSAs. Advancing the methodological frontier, this dissertation introduces a cutting-edge approach that integrates machine learning (ML) with kinetic Monte Carlo (KMC) simulations to efficiently investigate the controversial "sluggish diffusion" phenomenon using Ni-Fe concentrated alloys as the initial model systems. By developing an artificial neural network (ANN) model trained on pre-calculated migration barriers using the standard nudged elastic band (NEB) method, this approach enables precise, efficient, and on-the-fly predictions of vacancy migration barriers for arbitrary local atomic environments, including both random solution configuration and alloys with SROs. The diffusivities obtained from this ANN-KMC modeling closely align with independent molecular dynamics (MD) and temperature-accelerated dynamics (TAD) simulations at their accessible temperatures, but with a far better efficiency. The ANN-KMC approach is then extended to non-equiatomic FeNiCrCoCu HEAs. An improved ANN model is developed to predict vacancy migration barriers across a wide compositional range. This model, remarkably effective despite being trained solely on equiatomic HEA data, accurately predicts vacancy migration barriers in non-equiatomic compositions and their binary to quinary subsystems. This capability facilitates an extensive study of over 1,500 HEA compositions, uncovering the presence of sluggish diffusion in many non-equiatomic compositions. The analysis provides critical understanding of the diffusion behavior in a vast compositional space, offering invaluable insights for experimental alloy design and development. Lastly, the dissertation delves into interstitial-mediated diffusion in FeNiCrCoCu HEAs, confirming the presence of sluggish interstitial diffusion. A machine learning KMC (ML-KMC) method has been developed to simulate 〈100〉 dumbbell interstitial diffusion across various HEA compositions, closely replicating diffusion patterns as independent MD simulations. This novel ML-KMC approach offers a promising high-throughput method for studying HEAs, avoiding the expensive computational overhead associated with calculating migration barriers. The impact of the percolation effect of faster diffusing elements (Cr, Cu) is also analyzed. Regarding future research directions, two promising projects are evaluated. The first expands the ANN-KMC framework to render more accurate interstitial diffusion simulations, and the second focuses on developing a ML potential for an equiatomic CoCrFeMnNi HEA. The progresses and challenges are discussed.

In this study, we have investigated the relationship between the process-microstructure to predict and modify the material's properties. Understanding these relationships allows the identification and correction of processing deficiencies when the desired properties are not achieved, depending on the microstructure. Hence, the co-relation between process-microstructure-properties helped reduce the number of experiments, materials & tool costs and saved much time. In the case of high entropy alloys, friction stir welding (FSW) causes improved strength due to the formation of fine grain structure and phase transformation from f.c.c to h.c.p. The phase transformation is temperature sensitive and is studied with the help of differential scanning calorimetry (DSC) to calculate the enthalpy experimentally to obtain ΔGγ→ε. The second process discussed is heat treatment causing precipitation evolution. Fundamental investigations aided in understanding the influence of strengthening precipitates on mechanical properties due to the aging kinetics – solid solution and variable artificial aging temperature and time. Finally, in the third case, the effect of FSW parameters causes the thermal profile to be generated, which significantly influences the final microstructure and weld properties. Therefore, a computational model using COMSOL Multiphysics and TC-Prisma is developed to generate the thermal profile for different weld parameters to understand its effect on the microstructure, which would eventually affect and predict the final properties of the weld. The model's validation is done via DSC, TEM, and mechanical testing.

In this dissertation, tribo-corrosion behavior of several single-phase and multi-phase high entropy alloys were investigated. Tribo-corrosion of body centered cubic MoNbTaTiZr high entropy alloy in simulated physiological environment showed very low friction coefficient (~ 0.04), low wear rate (~ 10-8 mm3/Nm), body-temperature assisted passivation, and excellent biocompatibility with respect to stem cells and bone forming osteoblast cells. Tribo-corrosion resistance was evaluated for additively manufactured face centered cubic CoCrFeMnNi high entropy alloy in simulated marine environment. The additively manufactured alloy was found to be significantly better than its as-cast counterpart which was attributed to the refined microstructure and homogeneous elemental distribution. Additively manufactured CoCrFeMnNi showed lower wear rate, regenerative passivation, less wear volume loss, and nobler corrosion potential during tribo-corrosion test compared to its as-cast equivalent. Furthermore, in the elevated temperature (100 °C) tribo-corrosion environment, AlCoCrFeNi2.1 eutectic high entropy alloy showed excellent microstructural stability and pitting resistance with an order of magnitude lower wear volume loss compared to duplex stainless steel. The knowledge gained from tribo-corrosion response and stress-corrosion susceptibility of high entropy alloys was used in the development of bio-electrochemical sensors to sense implant degradation. The results obtained herewith support the promise of high entropy alloys in outperforming currently used structural alloys in the harsh tribo-corrosion environment.

High entropy alloys (HEAs) or complex concentrated alloys (CCAs) represent a new paradigm in structural alloy design. Molten salt corrosion behavior was studied for single-phase HEAs such as TaTiVWZr and HfTaTiVZr, and multi-phase HEAs such as AlCoCrFeNi2.1. De-alloying with porosity formation along the exposed surface and fluxing of unstable oxides were found to be primary corrosion mechanisms. Potentiodynamic polarization study was combined with systematic mass–loss study for TaTiVWZr, HfTaTiVZr, and AlCoCrFeNi2.1 as a function of temperature. Electrochemical impedance spectroscopy (EIS) was used for monitoring the corrosion of TaTiVWZr and HfTaTiVZr in molten fluoride salt at 650 oC. TaTiVWZr and AlCoCrFeNi2.1 showed low corrosion rate in the range of 5.5-7.5 mm/year and low mass-loss in the range of 35-40 mg/cm2 in molten chloride salt at 650 oC. Both TaTiVWZr and HfTaTiVZr showed similar mass loss in the range of 31-33 mg/cm2, which was slightly higher than IN 718 (~ 28 mg/cm2) in molten fluoride salt at 650 oC. Ta-W rich dendrite region in TaTiVWZr showed higher corrosion resistance against dissolution of alloying elements in the molten salt environment. AlCoCrFeNi2.1 showed higher resistance to galvanic corrosion compared to Duplex steel 2205 in molten chloride salt environment. These results suggest the potential use of HEAs/CCAs as structural materials in the molten salt environment for concentrating solar power and nuclear reactor systems.

In the present study both fusion based - laser powder bed fusion (LPBF), and solid state - additive friction stir deposition (AFSD) additive manufacturing processes were employed for the manufacturing of a metastable high entropy alloy (HEA), Fe40Mn20Co20Cr15Si5 (CS-HEA). A processing window was developed for the LPBF and AFSD processings of CS-HEA. In case of LPBF, formation of solidification related defects such as lack of fusion pores (for energy density ≤ 31.24 J/mm3) and keyhole pores (for energy density ≥ 75 J/mm3) were observed. Variation in processing conditions affected the microstructural evolution of the metastable CS-HEA; correlation between processing conditions and microstructure of the alloy is developed in the current study. The tendency to transform and twin near stress concentration sites provided excellent tensile and fatigue properties of the material despite the presence of defects in the material. Moreover, solid state nature of AFSD process avoids formation of solidification related defects. Defect free builds of CS-HEA using AFSD resulted in higher work hardening in the material. In summary, the multi-processing techniques used for CS-HEA in the present study showcase the capability of the AM process in tailoring the microstructure, i.e., grain size and phase fractions, both of which are extremely critical for the mechanical property enhancement of the alloy.

<p>Ni-based superalloys continue to exert themselves as the industry standards in high stress and highly corrosive/oxidizing environments, such as are present in a gas turbine engine, due to their excellent high temperature strengths, thermal and microstructural stabilities, and oxidation and creep resistances. Gas turbine engines are essential components for energy generation and propulsion in the modern age. However, Ni-based superalloys are reaching their limits in the operating conditions of these engines due to their melting onset temperatures, which is approximately 1300 °C. Therefore, a new class of materials must be formulated to surpass the capabilities Ni-based superalloys, as increasing the operating temperature leads to increased efficiency and reductions in fuel consumption and greenhouse gas emissions. One of the proposed classes of materials is termed refractory complex concentrated alloys, or RCCAs, which consist of 4 or more refractory elements (in this study, selected from: Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, and W) in equimolar or near-equimolar proportions. So far, there have been highly promising results with these alloys, including far higher melting points than Ni-based superalloys and outstanding high-temperature strengths in non-oxidizing environments. However, improvements in room temperature ductility and high-temperature oxidation resistance are still needed for RCCAs. Also, given the millions of possible alloy compositions spanning various combinations and concentrations of refractory elements, more efficient methods than just serial experimental trials are needed for identifying RCCAs with desired properties. A coupled computational and experimental approach for exploring a wide range of alloy systems and compositions is crucial for accelerating the discovery of RCCAs that may be capable of replacing Ni-based superalloys. </p> <p>In this thesis, the CALPHAD method was utilized to generate basic thermodynamic properties of approximately 67,000 Al-bearing RCCAs. The alloys were then down-selected on the basis of certain criteria, including solidus temperature, volume percent BCC phase, and aluminum activity. Machine learning models with physics-based descriptors were used to select several BCC-based alloys for fabrication and characterization, and an active learning loop was employed to aid in rapid alloy discovery for high hardness and strength. This method resulted in rapid identification of 15 BCC-based, four component, Al-bearing RCCAs exhibiting room-temperature Vickers hardness from 1% to 35% above previously reported alloys. This work exemplifies the advantages of utilizing Integrated Computational Materials Engineering- and Materials Genome Initiative-driven approaches for the discovery and design of new materials with attractive properties.</p> <p> </p> <p><br></p>