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1. Phase determination in dual phase steels via HREBSD‐based tetragonality mapping

   https://doi.org/10.1111/jmi.12980  

   Adams, Derrik ; Miles, Michael P. ; Homer, Eric R. ; Brown, Tyson ; Mishra, Raj K. ; Fullwood, David T. ( April 2021 , Journal of Microscopy)

   Electron Backscatter Diffraction (EBSD) is a widely used approach for characterising the microstructure of various materials. However, it is difficult to accurately distinguish similar (body centred cubic and body centred tetragonal, with small tetragonality) phases in steels using standard EBSD software. One method to tackle the problem of phase distinction is to measure the tetragonality of the phases, which can be done using simulated patterns and cross‐correlation techniques to detect distortion away from a perfectly cubic crystal lattice. However, small errors in the determination of microscope geometry (the so‐called pattern or projection centre) can cause significant errors in tetragonality measurement and lead to erroneous results. This paper utilises a new approach for accurate pattern centre determination via a strain minimisation routine across a large number of grains in dual phase steels. Tetragonality maps are then produced and used to identify phase and estimate local carbon content. The technique is implemented using both kinetically simulated and dynamically simulated patterns to determine their relative accuracy. Tetragonality maps, and subsequent phase maps, based on dynamically simulated patterns in a point‐by‐point and grain average comparison are found to consistently produce more precise and accurate results, with close to 90% accuracy for grain phase identification, when compared with an image‐quality identification method. The error in tetragonality measurements appears to be of the order of 1%, thus producing a commensurate ∼0.2% error in carbon content estimation. Such an error makes the technique unsuitable for estimation of total carbon content of most commercial steels, which often have carbon levels below 0.1%. However, even in the DP steel for this study (0.1 wt.% carbon) it can be used to map carbon in regions with higher accumulation (such as in martensite with nonhomogeneous carbon content).

   Electron Backscatter Diffraction (EBSD) is a widely used approach for characterising the microstructure of various materials. However, it is difficult to accurately distinguish similar (BCC and BCT) phases in steels using standard EBSD software due to the small difference in crystal structure. One method to tackle the problem of phase distinction is to measure the tetragonality, or apparent ‘strain’ in the crystal lattice, of the phases. This can be done by comparing experimental EBSD patterns with simulated patterns via cross‐correlation techniques, to detect distortion away from a perfectly cubic crystal lattice. However, small errors in the determination of microscope geometry (the so‐called pattern or projection centre) can cause significant errors in tetragonality measurement and lead to erroneous results. This paper utilises a new approach for accurate pattern centre determination via a strain minimisation routine across a large number of grains in dual phase steels. Tetragonality maps are then produced and used to identify phase and estimate local carbon content. The technique is implemented using both simple kinetically simulated and more complex dynamically simulated patterns to determine their relative accuracy. Tetragonality maps, and subsequent phase maps, based on dynamically simulated patterns in a point‐by‐point and grain average comparison are found to consistently produce more precise and accurate results, with close to 90% accuracy for grain phase identification, when compared with an image‐quality identification method. The error in tetragonality measurements appears to be of the order of 1%, thus producing a commensurate error in carbon content estimation. Such an error makes an estimate of total carbon content particularly unsuitable for low carbon steels; although maps of local carbon content may still be revealing.

   Application of the method developed in this paper will lead to better understanding of the complex microstructures of steels, and the potential to design microstructures that deliver higher strength and ductility for common applications, such as vehicle components.

    

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2. Adaptable physics-based super-resolution for electron backscatter diffraction maps

   https://doi.org/10.1038/s41524-022-00924-2  

   Jangid, Devendra K. ; Brodnik, Neal R. ; Goebel, Michael G. ; Khan, Amil ; Majeti, SaiSidharth ; Echlin, McLean P. ; Daly, Samantha H. ; Pollock, Tresa M. ; Manjunath, B. S. ( December 2022 , npj Computational Materials)

   In computer vision, single-image super-resolution (SISR) has been extensively explored using convolutional neural networks (CNNs) on optical images, but images outside this domain, such as those from scientific experiments, are not well investigated. Experimental data is often gathered using non-optical methods, which alters the metrics for image quality. One such example is electron backscatter diffraction (EBSD), a materials characterization technique that maps crystal arrangement in solid materials, which provides insight into processing, structure, and property relationships. We present a broadly adaptable approach for applying state-of-art SISR networks to generate super-resolved EBSD orientation maps. This approach includes quaternion-based orientation recognition, loss functions that consider rotational effects and crystallographic symmetry, and an inference pipeline to convert network output into established visualization formats for EBSD maps. The ability to generate physically accurate, high-resolution EBSD maps with super-resolution enables high-throughput characterization and broadens the capture capabilities for three-dimensional experimental EBSD datasets.

    

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3. Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns

   https://doi.org/10.1038/s41524-022-00939-9  

   Munshi, Joydeep ; Rakowski, Alexander ; Savitzky, Benjamin H. ; Zeltmann, Steven E. ; Ciston, Jim ; Henderson, Matthew ; Cholia, Shreyas ; Minor, Andrew M. ; Chan, Maria K. Y. ; Ophus, Colin ( December 2022 , npj Computational Materials)

   A fast, robust pipeline for strain mapping of crystalline materials is important for many technological applications. Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions, but this technique is limited when the electron beam undergoes multiple scattering. Deep-learning methods have the potential to invert these complex signals, but require a large number of training examples. We implement a Fourier space, complex-valued deep-neural network, FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures, orientations, thicknesses, and microscope parameters, which are augmented with experimental artifacts. We evaluated FCU-Net against simulated and experimental datasets, where it substantially outperforms conventional analysis methods. Our code, models, and training library are open-source and may be adapted to different diffraction measurement problems.

    

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4. Parametric simulation of electron backscatter diffraction patterns through generative models

   https://doi.org/10.1038/s41524-023-01143-z  

   Ding, Zihao ; De Graef, Marc ( October 2023 , npj Computational Materials)

   Recently, discriminative machine learning models have been widely used to predict various attributes from Electron Backscatter Diffraction (EBSD) patterns. However, there has never been any generative model developed for EBSD pattern simulation. On one hand, the training of generative models is much harder than that of discriminative ones; On the other hand, numerous variables affecting EBSD pattern formation make the input space high-dimensional and its relationship with the distribution of backscattered electrons complicated. In this study, we propose a framework (EBSD-CVAE/GAN) with great flexibility and scalability to realize parametric simulation of EBSD patterns. Compared with the frequently used forward model, EBSD-CVAE/GAN can take variables more than just orientation and generate corresponding EBSD patterns in a single run. The accuracy and quality of generated patterns are systematically evaluated. The model does not only summarize a distribution of backscattered electrons at a higher level, but also mitigates data scarcity in this field.

    

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5. Untangling the Fundamental Electronic Origins of Non‐Local Electron–Phonon Coupling in Organic Semiconductors

   https://doi.org/10.1002/adfm.202303701  

   Banks, Peter A. ; D'Avino, Gabriele ; Schweicher, Guillaume ; Armstrong, Jeff ; Ruzié, Christian ; Chung, Jong Won ; Park, Jeong‐Il ; Sawabe, Chizuru ; Okamoto, Toshihiro ; Takeya, Jun ; et al ( May 2023 , Advanced Functional Materials)

   Organic semiconductors with distinct molecular properties and large carrier mobilities are constantly developed in attempt to produce highly‐efficient electronic materials. Recently, designer molecules with unique structural modifications have been expressly developed to suppress molecular motions in the solid state that arise from low‐energy phonon modes, which uniquely limit carrier mobilities through electron–phonon coupling. However, such low‐frequency vibrational dynamics often involve complex molecular dynamics, making comprehension of the underlying electronic origins of electron–phonon coupling difficult. In this study, first a mode‐resolved picture of electron–phonon coupling in a series of materials that are specifically designed to suppress detrimental vibrational effects, is generated. From this foundation, a method is developed based on the crystalline orbital Hamiltonian population (COHP) analyses to resolve the origins—down to the single atomic‐orbital scale—of surprisingly large electron–phonon coupling constants of particular vibrations, explicitly detailing the manner in which the intermolecular wavefunction overlap is perturbed. Overall, this approach provides a comprehensive explanation into the unexpected effects of less‐commonly studied molecular vibrations, revealing new aspects of molecular design that should be considered for creating improved organic semiconducting materials.

    

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