Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method

Souiyah, Miloud

doi:10.1080/23311916.2023.2232596

Cited by 1 publication

(1 citation statement)

References 45 publications

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“…Under normal circumstances, it is necessary to normalize the original data, and then perform the anti-normalization processing after the calculation is completed. Currently, the following neural network models have been used to predict the photocatalytic performance of photocatalysts: Perceptron (P), feed forward (FF), radial basis network (RBF), deep feed forward (DFF), recurrent neural network (RNN), long/short term memory (LSTM), restricted BM (RBM), deep convolutional network (DCN), generative adversarial network (GAN), extreme learning machine (ELM), echo state network (ESN), and support vector machine (SVM) [49][50][51][52][53][54][55][56][57][58]. In addition to the models mentioned above, the backpropagation (BP) neural network model is the most popular model for predicting the photocatalytic performance of various photocatalysts [59][60][61].…”

Section: Neural Network Model Suitable For Photocatalyst Developmentmentioning

confidence: 99%

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Wang,

Mo,

et al. 2024

Catalysts

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Photocatalysts have made great contributions to the degradation of pollutants to achieve environmental purification. The traditional method of developing new photocatalysts is to design and perform a large number of experiments to continuously try to obtain efficient photocatalysts that can degrade pollutants, which is time-consuming, costly, and does not necessarily achieve the best performance of the photocatalyst. The rapid development of photocatalysis has been accelerated by the rapid development of artificial intelligence. Intelligent algorithms can be utilized to design photocatalysts and predict photocatalytic performance, resulting in a reduction in development time and the cost of new catalysts. In this paper, the intelligent algorithms for photocatalyst design and photocatalytic performance prediction are reviewed, especially the artificial neural network model and the model optimized by an intelligent algorithm. A detailed discussion is given on the advantages and disadvantages of the neural network model, as well as its application in photocatalysis optimized by intelligent algorithms. The use of intelligent algorithms in photocatalysis is challenging and long term due to the lack of suitable neural network models for predicting the photocatalytic performance of photocatalysts. The prediction of photocatalytic performance of photocatalysts can be aided by the combination of various intelligent optimization algorithms and neural network models, but it is only useful in the early stages. Intelligent algorithms can be used to design photocatalysts and predict their photocatalytic performance, which is a promising technology.

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