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  1. Free, publicly-accessible full text available December 27, 2024
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    The stability of supported metal nanoparticles determines the activity and lifetime of heterogeneous catalysts. Catalysts can destabilize through several thermodynamic and kinetic pathways, and the competition between these mechanisms complicates efforts to quantify and predict the overall evolution of supported nanoparticles in reactive environments. Pairing in situ transmission electron microscopy with unsupervised machine learning, we quantify the destabilization of hundreds of supported Au nanoparticles in real-time to develop a model describing the observed particle evolution as a competition between evaporation and surface diffusion. Data mining of particle evolution statistics allows us to determine physically reasonable values for the model parameters, quantify the particle size at which the Gibbs–Thomson pressure accelerates the evaporation process, and explore how individual particle interactions deviate from the mean-field model. This approach can be applied to a wide range of supported nanoparticle systems, allowing quantitative insight into the mechanisms that control their evolution in reactive environments. 
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    Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design. 
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