Prediction of thermal boundary resistance by the machine learning method

Zhan, Tianzhuo; Fang, Lei; Xu, Yibin

doi:10.1038/s41598-017-07150-7

Cited by 82 publications

(87 citation statements)

References 77 publications

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“…Finally, the overall thermal resistance of a thermoelectric device is the sum of the bulk thermal resistance and the thermal boundary resistance (TBR) at the interfaces of the materials. Zhan et al have developed four ML models to predict experimentally measured TBR. The four models deployed different algorithms, namely, generalized linear regression (GLR), LASSO‐GLR, GPR, and SVR.…”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

281

View full text Add to dashboard Cite

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“…Htcp (heat capacity), thcd (thermal conductivity), debye (Debye temperature), melt (melting point), dens (density), spdl (speed of sound longitudinal), spdt (speed of sound transverse), elam (elastic modulus), blkm (bulk modulus), thex (thermal expansion coefficient), and unitc (unit cell volume). (Reprinted with permission from Reference . Copyright 2019 Nature Research)…”

Section: Property Predictions Using Supervised Algorithmsmentioning

confidence: 99%

Machine learning and artificial neural network accelerated computational discoveries in materials science

Yang

Hou

Jiang

et al. 2019

WIREs Comput Mol Sci

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Artificial intelligence (AI) has been referred to as the "fourth paradigm of science," and as part of a coherent toolbox of data-driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state-of-the-art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML-based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.

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“…Next, a metamodeling approach is introduced, where a surrogate model substitutes the most expensive single scale model. The metamodel can be constructed by applying, for example, a data-driven approach, like Gaussian process regression [26,18,28], or using a spectral approach, like the stochastic Galerkin method [10]. Since only one component of the multiscale model is approximated by the surrogate, the resulting error in the model output can be small enough to still be able to obtain reliable uncertainty estimates.…”

Section: Introductionmentioning

confidence: 99%

Semi-intrusive uncertainty propagation for multiscale models

Nikishova

Hoekstra

2019

Journal of Computational Science

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A family of semi-intrusive Uncertainty Quantification (UQ) methods for multiscale models is introduced. The methods are semi-intrusive in the sense that inspection of the model is limited up to the level of the single scale systems, and viewing these single scale components as black-boxes. The goal is to estimate uncertainty in the result of multiscale models at a reduced amount of time as compared to black-box Monte Carlo (MC). In the resulting semi-intrusive MC method, the required number of samples of an expensive single scale model is minimized in order to reduce the execution time for the overall UQ. In the metamodeling approach the expensive model component is replaced completely by a computationally much cheaper surrogate model. These semi-intrusive algorithms have been tested on two case studies based on reaction-diffusion dynamics. The results demonstrate that the proposed semiintrusive methods can result in a significant reduction of the computational time for multiscale UQ, while still computing accurately the estimates of uncertainties. The semi-intrusive methods can therefore be a valid alternative, when uncertainties of a multiscale model cannot be estimated by the blackbox MC methods in a feasible amount of time.

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Prediction of thermal boundary resistance by the machine learning method

Cited by 82 publications

References 77 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Machine learning and artificial neural network accelerated computational discoveries in materials science

Semi-intrusive uncertainty propagation for multiscale models

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