Shiqiang Hao

Hydrogen interactions with microstructural defects play a critical role in hydrogen-induced degradation of austenitic stainless steels, yet a quantitative description of hydrogen solubility across defects of different dimensionalities remains limited. This work combines a machine learning interatomic potential with grand canonical Monte Carlo simulations to systematically evaluate hydrogen solubility in the Fe0.7Cr0.19Ni0.11 solid solution, spanning the bulk phase, two-dimensional grain boundaries, and one-dimensional edge dislocations. Hydrogen incorporation is quantified under realistic temperature and pressure conditions by explicitly accounting for the statistical distribution of hydrogen binding energies associated with local atomic environments. This work introduces the concept of excess hydrogen solubility to provide a unified, quantitative measure of hydrogen segregation relative to the bulk phase. These results reveal that twist grain boundaries exhibit negative excess solubility, whereas tilt grain boundaries and edge dislocations act as effective hydrogen traps due to defect-induced structural distortion and strain fields. The observed trends are governed by the thermodynamic balance between hydrogen chemical potential and defect specific binding energy distributions, rather than defect dimensionality alone. This work establishes a transferable thermodynamic framework for predicting hydrogen defect interactions in chemically complex alloys, with direct implications for understanding and mitigating hydrogen-induced degradation.

Advanced thermal environmental barrier coatings (TEBCs) play a critical role in high‐temperature applications across modern engineering fields, particularly in gas turbines and aerospace components where heat management and mechanical stability are essential. This review presents a comprehensive overview of computational approaches, primarily leveraging density functional theory, to investigate key material properties of TEBC systems, including phase stability, elastic properties, coefficient of thermal expansion, lattice thermal conductivity, and intrinsic defects. By aiming to improve design and optimization of TEBC systems, the article discusses the practical applications of these methodologies alongside their inherent advantages and limitations. Emphasis is placed on the importance for researchers to recognize the constraints of each computational approach to ensure accurate and reliable predictions across diverse material systems.

Current and future high‐temperature materials rely on oxide layers formed during service or applied as coatings to protect components from oxidation‐ and corrosion‐induced damage. Understanding of defect diffusion within these oxides is often lacking, despite the central role of these processes in dictating material performance. This study investigates the intrinsic defect diffusion mechanisms in five oxides within the technologically relevant Y–Al–O system (Al ₂ O ₃ , Y ₃ Al ₅ O ₁₂ , YAlO ₃ , Y ₄ Al ₂ O ₉ , and Y ₂ O ₃ ). Using density functional theory calculations, defect formation energies are analyzed under varying oxygen conditions to identify the most dominant defects in these oxides. Based on the formation energies under oxygen‐rich conditions, our investigations into the diffusivity of energetically favorable defects reveal that Al ₂ O ₃ exhibits the lowest defect diffusivity. In contrast, Y ₂ O ₃ has the highest defect diffusivity due to its extensive channel structure. Additionally, we observe a trend of increasing diffusivity with higher Y ₂ O ₃ content in the Y–Al–O series. In particular, results suggest that alloying Al ₂ O ₃ with Y ₂ O ₃ should ideally maintain a ratio of more than 5:3 to prevent significant increases in diffusivity and commensurate susceptibility to diffusion‐controlled oxidation and corrosion damage. Ultimately, this research enhances our understanding of intrinsic defect diffusion mechanisms in complex oxides aimed at designing next‐generation materials resistant to degradation in extreme environments.

Gaining atomistic understanding of mechanical behavior of heat-resistant structural materials such as Fe–Cr–Ni-based alloys requires an approach with an accuracy close to density functional theory (DFT) that considers the intrinsic properties of the bulk lattice and important defects such as stacking faults, grain boundaries, and surfaces. This work aims to develop reliable machine learning interatomic potential (MLIAP) at cross-scale for Fe–Cr–Ni ternary alloys with a focus on the face-centered-cubic (fcc) solid solution structure. Leveraging the advantages of moment tensor potentials, which typically necessitate a relatively small training dataset and enable rapid calculations using the large-scale atomic/molecular massively parallel simulator package, we ensure the stability and accuracy of the trained potentials. Important defects such as stacking faults, grain boundaries, and surfaces for wide-range compositions are investigated. Structural, thermal, elastic, and defect properties are determined from molecular dynamics simulations comprising several thousand atoms, generated via canonical Monte Carlo simulations guided by the trained potential. The trained potential allows efficient atomic simulations of structural, thermal, and mechanical properties of fcc Fe–Cr–Ni solid solution alloys as a function of composition and temperature. Therefore, the MLIAP approach represents a major advancement from DFT calculations that are limited to small simulation sizes and traditional molecular dynamics simulations using relatively low accuracy potentials. Furthermore, this work outlines a practical foundation for further investigating the structural evolution and mechanical behavior of austenitic stainless steel and nickel-based alloys in a wide array of applications in extreme environments.

Environmental barrier coatings (EBC) are crucial for the use of SiC-based ceramic matrix composites in high-temperature combustion environments, yet knowledge of oxygen diffusion in these coatings is limited. This study investigates oxygen diffusion dynamics in the β-RE₂Si₂O₇ system to minimize oxygen penetration in rare earth disilicates. We analyze defect formation energy under varying oxygen conditions, identifying key diffusion mechanisms. In oxygen-rich environments, the most favorable neutral interstitial oxygen diffuses along the [110] direction. In oxygen-poor conditions, neutral oxygen vacancies rotate around Y and Si atoms, exhibiting a diffusivity of 6.59×10⁻²² m²/s at 1500 K for β-Yb₂Si₂O₇. Under intermediate oxygen levels, charged interstitial oxygen diffuses via concerted interstitialcy along the [001] direction with a diffusivity of 6.21×10⁻¹⁷ m²/s. Additionally, alloying rare earth Y with Er and Yb increases diffusion barriers, contributing to improved EBC performance in extreme environments. The insights gained provides valuable guidance for designing robust coatings tailored to withstand extreme operational environments.

Computational discovery and comprehensive property evaluation of advanced thermal/environmental barrier coatings of high-entropy rare earth disilicates alloys tuned by strong anharmonicity of Eu ₂ Si ₂ O ₇ .