A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

Kautz, Elizabeth J.; Hagen, Alexander; Johns, Jesse; Burkes, Douglas E.

doi:10.1016/j.commatsci.2019.01.044

Cited by 31 publications

(16 citation statements)

References 60 publications

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“…[36] Thus, while machine learning techniques have shown initial success, [24,[37][38][39][40][41] both more data and novel approaches are needed in order to explore the vast materials space.…”

Section: Introductionmentioning

confidence: 99%

Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics

Zhu

Gong

et al. 2021

Energy Environ. Sci.

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Section: Introductionmentioning

confidence: 99%

Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics

Zhu

Gong

et al. 2021

Energy Environ. Sci.

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“…Kautz et al [114] employed a deep neural network to predict the thermal conductivity of the uranium-molybdenum system. An input vector contained the following information: molybdenum concentration and uranium enrichment at both the beginning and end of life, 235 U depletion and measured depletion, fission density and measured fission density, fission power, surface heat flux, neutron, and average advanced test reactor loop temperature.…”

Section: Applications Of Ai Techniques For Modeling Of Metals and Their Compositesmentioning

confidence: 99%

Artificial Intelligence in Materials Modeling and Design

Huang

Liew

Ademiloye

et al. 2020

Arch Computat Methods Eng

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In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented. Highlights We present an up to date review of the application of artificial intelligence in materials modeling and design.  We comprehensively discuss past and recent applications in modeling and design of polymers, metals, ceramics and other materials. We identify current research focal points, challenges, and opportunities for the application of artificial intelligence in materials modeling and design.

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“… 32 , 33 , 34 , 35 They have seen diverse applications ranging from the discovery of new materials 36 , 37 , 38 , 39 , 40 to the predictions of materials’ properties, 41 , 42 , 43 , 44 , 45 the development of accurate and efficient potentials for atomistic simulations, 46 , 47 , 48 , 49 microscopic and spectroscopic data analysis and processing, 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 and effective inference of a material’s properties from a limited experimental dataset. 63 , 64 A large number of these works are devoted to material microstructure, with encouraging results, including microstructure classification and quantification, 50 , 51 , 52 , 53 , 54 , 65 , 66 , 67 image segmentation, 55 , 56 predictions of microstructure-property relations, 57 , 68 , 69 , 70 mapping processing-microstructure relations, 71 , 72 , 73 , 74 microstructure optimization, 75 , 76 , 77 and equilibrium configuration prediction. 78 Datasets in these works are mainly in the form of static microstructure images.…”

Section: Introductionmentioning

confidence: 99%

“…[32][33][34][35] They have seen diverse applications ranging from the discovery of new materials [36][37][38][39][40] to the predictions of materials' properties, [41][42][43][44][45] the development of accurate and efficient potentials for atomistic simulations, [46][47][48][49] microscopic and spectroscopic data analysis and processing, [50][51][52][53][54][55][56][57][58][59][60][61][62] and effective inference of a material's properties from a limited experimental dataset. 63,64 A large number of these works are devoted to material microstructure, with encouraging results, including microstructure classification and quantification, [50][51][52][53][54][65][66][67] image segmentation, 55,56 predictions of microstructure-property relations, 57,[68][69]…”

Section: Introductionmentioning

confidence: 99%

Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks

et al. 2021

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Summary Microstructural evolution is a key aspect of understanding and exploiting the processing-structure-property relationship of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we demonstrate that convolutional recurrent neural networks can learn the underlying physical rules and replace PDE-based simulations in the prediction of microstructure phenomena. Neural nets are trained by self-supervised learning with image sequences from simulations of several common processes, including plane-wave propagation, grain growth, spinodal decomposition, and dendritic crystal growth. The trained networks can accurately predict both short-term local dynamics and long-term statistical properties of microstructures assessed herein and are capable of extrapolating beyond the training datasets in spatiotemporal domains and configurational and parametric spaces. Such a data-driven approach offers significant advantages over PDE-based simulations in time-stepping efficiency and offers a useful alternative, especially when the material parameters or governing PDEs are not well determined.

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A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

Cited by 31 publications

References 60 publications

Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics

Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics

Artificial Intelligence in Materials Modeling and Design

Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks

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