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  1. Abstract

    Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

     
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  2. Free, publicly-accessible full text available April 19, 2025
  3. Compositionally complex oxides (CCOs) are an emerging class of materials encompassing high entropy and entropy stabilized oxides. These promising advanced materials leverage tunable chemical bond structure, lattice distortion, and chemical disorder for unprecedented properties. Grain boundary (GB) and point defect segregation to GBs are relatively understudied in CCOs even though they can govern macroscopic material properties. For example, GB segregation can govern local chemical (dis)order and point defect distribution, playing a critical role in electrochemical reaction kinetics, and charge and mass transport in solid electrolytes. However, compared with conventional oxides, GBs in multi-cation CCO systems are expected to exhibit more complex segregation phenomena and, thus, prove more difficult to tune through GB design strategies. Here, GB segregation was studied in a model perovskite CCO LaFe0.7Ni0.1Co0.1Cu0.05Pd0.05O3−x textured thin film by (sub-)atomic-resolution scanning transmission electron microscopy imaging and spectroscopy. It is found that GB segregation is correlated with cation reducibility—predicted by an Ellingham diagram—as Pd and Cu segregate to GBs rich in oxygen vacancies (VO··). Furthermore, Pd and Cu segregation is highly sensitive to the concentration and spatial distribution of VO·· along the GB plane, as well as fluctuations in atomic structure and elastic strain induced by GB local disorder, such as dislocations. This work offers a perspective of controlling segregation concentration of CCO cations to GBs by tuning reducibility of CCO cations and oxygen deficiency, which is expected to guide GB design in CCOs.

     
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    Free, publicly-accessible full text available April 25, 2025
  4. Free, publicly-accessible full text available January 29, 2025
  5. Free, publicly-accessible full text available December 16, 2024
  6. Free, publicly-accessible full text available August 16, 2024
  7. Free, publicly-accessible full text available August 1, 2024
  8. Refractory multi-principal element alloys exhibiting promising mechanical properties such as excellent strength retention at elevated temperatures have been attracting increasing attention. Although their inherent chemical complexity is considered a defining feature, a challenge arises in predicting local chemical ordering, particularly in grain boundary regions with an enhanced structural disorder. In this study, we use atomistic simulations of a large group of bicrystal models to sample a wide variety of interfacial sites (grain boundary) in NbMoTaW and explore emergent trends in interfacial segregation and the underlying structural and chemical driving factors. Sampling hundreds of bicrystals along the [001] symmetric tilt axis and analyzing more than one hundred and thirty thousand grain boundary sites with a variety of local atomic environments, we uncover segregation trends in NbMoTaW. While Nb is the dominant segregant, more notable are the segregation patterns that deviate from expected behavior and mark situations where local structural and chemical driving forces lead to interesting segregation events. For example, incomplete depletion of Ta in low-angle boundaries results from chemical pinning due to favorable local compositional environments associated with chemical short-range ordering. Finally, machine learning models capturing and comparing the structural and chemical features of interfacial sites are developed to weigh their relative importance and contributions to segregation tendency, revealing a significant increase in predictive capability when including local chemical information. Overall, this work, highlighting the complex interplay between the local grain boundary structure and chemical short-range ordering, suggests tunable segregation and chemical ordering by tailoring grain boundary structure in multi-principal element alloys. 
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