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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.

 

Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures. 

To fully leverage the power of image simulation to corroborate and explain patterns and structures in atomic resolution microscopy, an initial correspondence between the simulation and experimental image must be established at the outset of further high accuracy simulations or calculations. Furthermore, if simulation is to be used in context of highly automated processes or high‐throughput optimization, the process of finding this correspondence itself must be automated. In this work, “ingrained,” an open‐source automation framework which solves for this correspondence and fuses atomic resolution image simulations into the experimental images to which they correspond, is introduced. Herein, the overall “ingrained” workflow, focusing on its application to interface structure approximations, and the development of an experimentally rationalized forward model for scanning tunneling microscopy simulation are described.

 

Synthetic two-dimensional polymorphs of boron, or borophene, have attracted attention because of their anisotropic metallicity, correlated-electron phenomena, and diverse superlattice structures. Although borophene heterostructures have been realized, ordered chemical modification of borophene has not yet been reported. Here, we synthesize “borophane” polymorphs by hydrogenating borophene with atomic hydrogen in ultrahigh vacuum. Through atomic-scale imaging, spectroscopy, and first-principles calculations, the most prevalent borophane polymorph is shown to possess a combination of two-center–two-electron boron-hydrogen and three-center–two-electron boron-hydrogen-boron bonds. Borophane polymorphs are metallic with modified local work functions and can be reversibly returned to pristine borophene through thermal desorption of hydrogen. Hydrogenation also provides chemical passivation because borophane reduces oxidation rates by more than two orders of magnitude after ambient exposure.

 

The deposition of protective coatings on the spinel LiMn₂O₄(LMO) lithium‐ion battery cathode is effective in reducing Mn dissolution from the electrode surface. Although protective coatings positively affect LMO cycle life, much remains to be understood regarding the interface formed between these coatings and LMO. Using operando powder X‐ray diffraction with Rietveld refinement, it is shown that, in comparison to bare LMO, the lattice parameter of a model Au‐coated LMO cathode is significantly reduced upon relithiation. Less charge passes through Au‐coated LMO in comparison to bare LMO, suggesting that the reduced lattice parameter is associated with decreased Li⁺solubility in the Au‐coated LMO. Density functional theory calculations show that a more Li⁺‐deficient near‐surface is thermodynamically favorable in the presence of the Au coating, which may further stabilize these cathodes through suppressing formation of the Jahn–Teller distorted Li₂Mn₂O₄phase at the surface. Electronic structure and chemical bonding analyses show enhanced hybridization between Au and LMO for delithiated surfaces leading to partial oxidation of Au upon delithiation. This study suggests that, in addition to transition metal dissolution from electrode surfaces, protective coating design must also balance potential energy effects induced by charge transfer at the electrode‐coating interface.