Application of Artificial Neural Networks for Catalysis: A Review

Li, Hao; Zhang, Zhien; Liu, Zhijian

doi:10.3390/catal7100306

Cited by 198 publications

(112 citation statements)

References 128 publications

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“…An MLP is a layered architecture and in each layer the inputs undergo an affine transformation followed by a nonlinear activation function. Their ability to be universal function approximators makes them extremely useful for materials property predictions, if sufficient training data exist. MLPs have also been used in combination with local environment features to develop interatomic potentials .…”

Section: Model Selection and Trainingmentioning

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: Model Selection and Trainingmentioning

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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“…The ANN is an artificial intelligent model that is developed to mimic the pattern of processing information by the human brain [42]. The neural network configuration processes a large number of interlinked units arranged in layers.…”

Section: Artificial Neural Network Configurationsmentioning

confidence: 99%

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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This study investigates the applicability of the Leven-Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H 2 production by methane dry reforming over a Co/Pr 2 O 3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH 4 partial pressure, CO 2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H 2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven-Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H 2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R 2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H 2 production were strongly correlated with the observed values.To overcome these challenges, several supported metal-based catalysts have been developed and tested. An extensive review by Abdullah et al. [10] revealed that supported nickel (Ni) catalysts have been mostly investigated for methane dry reforming due to its high catalytic performance. Nevertheless, the Ni-based catalysts are very prone to sintering and carbon deposition [11]. On the other hand, cobalt (Co)-based catalysts which have a comparative activity to Ni have been reported to show superior stability compare to Ni under the same process condition [12,13]. In our previous studies, the use of rare-earth metal oxide-supported Co catalysts for CO-rich hydrogen production showed considerable activity and stability [14][15][16]. However, one major challenge is understanding the kinetics of the methane dry reforming in terms of the rate of H 2 and CO production due to variations in the chemical composition of the various catalysts [17]. This challenge can be overcome by employing an artificial intelligence modeling approach for a better understanding of the process parameters [18,19]. Processes with non-linear and complex relationships between the input and the output parameters are often encountered in real life processes. The better understanding of the non-linear relationship between the input and the output parameters of the process can further be utilized to optimize the process operation and create the basis for the theoretical framework, process automation, and upscaling [20].An artificial intelligence modeling approach using an artificial neural network (ANN) has been widely employed for different catalytic processes, such as hydrodesulfurization [20], methanol steam reforming, glycerol steam reforming [2...

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“…人工神经网络 [25,26] [28] 之中, 包括金属有机框架材料 [29] (MOFs)、软物质及生物材料 [30] 、锂离子电池材料 [31,32] 、热电材料 [33,34] 、催化材料 [35,36] 、碳材料 [37] 等等. 除了可以有效加快新型材料 [40] 应用机器学习中的不同算法对硅酸盐正极材料进行晶体结构上的分类; 在材料的微观性质方面, Fujimura等人 [41] 用支持向量机算法来预测全固态电池中锂离子导体材料的离子电导率, 另外也有文章报道应用机器学习来筛选石榴石型 [42] 、橄榄石型 [43,44] 以及磷酸盐型 [45] 的电极材料; 在材料的宏观性能方面, 机器学习也被用于研究锂离子电池的循环性能, 包括使用遗传算法 [46] 、支持向量机 [47,48] 、粒子滤波算法 [49] 等在内的多种算法被用于评估锂离子电池的剩余有效寿命以及容量衰减情况 [50][51][52][53] .…”

Section: 人工神经网络unclassified

Applying machine learning to accelerate new materials development

Wu¹,

Sun²

2018

Sci. Sin.-Phys. Mech. Astron.

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Application of Artificial Neural Networks for Catalysis: A Review

Cited by 198 publications

References 128 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

Applying machine learning to accelerate new materials development

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