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Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deformation and crack propagation. The geometry of microstructure is translated into a scalar of elastic field as an intermediate variable, and then, the crack path is predicted. The neural network precisely characterizes the strong interactions among pore structures, the multiscale behaviors of fracture, and the discontinuous essence of crack propagation. Crack paths in random porous media are accurately predicted by simply inputting the images of targets, without inputting any additional input physical information. The prediction model enjoys an outstanding performance with a prediction accuracy of 90.25% and possesses a robust generalization capability. The accuracy of the present model is a record so far, and the prediction is accomplished within a second. This study opens an avenue to high-throughput evaluation of the fracture behaviors of heterogeneous materials with complex geometries. [ABSTRACT FROM AUTHOR]

Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deformation and crack propagation. The geometry of microstructure is translated into a scalar of elastic field as an intermediate variable, and then, the crack path is predicted. The neural network precisely characterizes the strong interactions among pore structures, the multiscale behaviors of fracture, and the discontinuous essence of crack propagation. Crack paths in random porous media are accurately predicted by simply inputting the images of targets, without inputting any additional input physical information. The prediction model enjoys an outstanding performance with a prediction accuracy of 90.25% and possesses a robust generalization capability. The accuracy of the present model is a record so far, and the prediction is accomplished within a second. This study opens an avenue to high-throughput evaluation of the fracture behaviors of heterogeneous materials with complex geometries., Competing Interests: Competing interests statement:The authors declare no competing interest.

• The catalytic performance of Ni loaded on different supports was studied. • The Ni/Al 0.5 Ce 0.5 O x catalysts exhibited high catalytic activity. • The introduction of Ce reduced the energy barrier of N associative decomposition. • A potential low-cost approach for NH 3 partial cracking was provided. Partial cracking is a feasible strategy to improve ammonia (NH 3) combustion. The nickel (Ni) based catalysts have become promising non-noble metal NH 3 decomposition catalysts owing to the low cost and decent performance. In order to meet the demand of reducing cost and expanding production, a batch of Ni catalysts were obtained by using low-cost supports and traditional impregnation preparation method. It was found that the Al 2 O 3 was the optimal support for Ni catalysts by comparing the catalytic performance. On the basis of Ni/Al 2 O 3 catalysts, the catalytic activity was further improved by introducing cerium (Ce) modification. The catalytic performance of Ce-doped catalysts enhanced at first and then decreased with the rise of Ce contents. Among all of catalysts in this work, the Ni/Al 0.5 Ce 0.5 O x catalysts possessed the highest activity and the lowest apparent activation energy. The characterization results suggested that the addition of Ce could destroy the pore structure of Al 2 O 3 supports, but it might promote the formation of more B5 active sites. The NH 3 temperature programmed desorption (NH 3 -TPD) results implied that the introduction of Ce strengthened the strong interactions between meatal and support, which improved the activity and stability of the Ce-doped catalysts. The Ni/Al 0.5 Ce 0.5 O x catalysts were proved to possess excellent resistance to the high temperature sintering through the 32 h's thermal stability test. This study provides a feasible path for preparing low-cost Ni catalysts for NH 3 decomposition. [ABSTRACT FROM AUTHOR]