Ensemble Machine‐Learning‐Based Analysis for In Situ Electron Diffraction

Ge, Ming; Liu, Xiaozhi; Zhao, Zhicheng; Su, Fei; Gu, Lin; Su, Dong

doi:10.1002/adts.202100337

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…It requires precise alignment and a highly parallel electron beam . Furthermore, adequate compensation of elliptical distortions is essential, which can nowadays be achieved by effective machine learning algorithms . Those high-quality electron diffraction patterns can then also be used for pair distribution function (PDF) analysis. ,, This total scattering approach allows us to conclude on the nearest neighbors (local structures), domain, and particle sizes.…”

Section: Imaging and Spectroscopy In Operando Em Studiesmentioning

confidence: 99%

Operando Electron Microscopy of Catalysts: The Missing Cornerstone in Heterogeneous Catalysis Research?

Chee,

Lunkenbein,

Schlögl

et al. 2023

Chem. Rev.

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Heterogeneous catalysis in thermal gas-phase and electrochemical liquid-phase chemical conversion plays an important role in our modern energy landscape. However, many of the structural features that drive efficient chemical energy conversion are still unknown. These features are, in general, highly distinct on the local scale and lack translational symmetry, and thus, they are difficult to capture without the required spatial and temporal resolution. Correlating these structures to their function will, conversely, allow us to disentangle irrelevant and relevant features, explore the entanglement of different local structures, and provide us with the necessary understanding to tailor novel catalyst systems with improved productivity. This critical review provides a summary of the still immature field of operando electron microscopy for thermal gas-phase and electrochemical liquid-phase reactions. It focuses on the complexity of investigating catalytic reactions and catalysts, progress in the field, and analysis. The forthcoming advances are discussed in view of correlative techniques, artificial intelligence in analysis, and novel reactor designs.

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Section: Imaging and Spectroscopy In Operando Em Studiesmentioning

confidence: 99%

Operando Electron Microscopy of Catalysts: The Missing Cornerstone in Heterogeneous Catalysis Research?

Chee,

Lunkenbein,

Schlögl

et al. 2023

Chem. Rev.

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“…In recent years, machine learning (ML) has emerged as a powerful tool for addressing a wide range of scientific challenges, including mechanics of materials research, [22][23][24][25][26][27][28] especially predicting material properties and understanding the underlying relationships between material composition, structure, and performance. [29][30][31][32] By leveraging large datasets and advanced algorithms, ML models can identify and learn complex patterns in the data, providing a new avenue for the study and prediction of hydrogel fracture behavior.…”

Section: Introductionmentioning

confidence: 99%

Fracture Prediction of Hydrogel Using Machine Learning and Inhomogeneous Multiscale Network

Zheng,

You,

Lam

et al. 2024

Advcd Theory and Sims

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Hydrogels are soft polymeric materials with promising applications in biomedical fields. Understanding their fracture behavior is crucial for optimizing device design and performance. However, predicting hydrogel fracture is challenging due to the complex interplay between material properties and environmental factors. In this study, a machine learning (ML) approach to predict hydrogel fracture behavior is presented. A multiscale hydrogel fracture model is developed to generate simulation data, which is used to train a predictive neural network model. The ML model utilizes a hierarchical architecture of convolution long short‐term memory units to capture spatial and temporal dependencies in the data. Model predictions are found to closely match simulation results with high accuracy, demonstrating the ability to learn complex fracture processes. Comparison of crack lengths shows the model can generalize across different material parameters. This work highlights the potential of ML for advancing the understanding of hydrogel fracture and soft matter failure. The presented approach provides an efficient framework for predicting fracture in complex materials and systems.

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