Application of Artificial Neural Networks to Combinatorial Catalysis: Modeling and Predicting ODHE Catalysts

Assessment of Predictive Ability of Artificial Neural Networks Using Holographic Mapping

“…ANNs are excellent for non-linear regressions. Their pattern recognition and predictive ability make them especially beneficial in different optimization tasks, such as optimization of heterogeneous catalysts libraries [1][2][3][4][5][6][7]. Based on input variables, such as composition, preparation and testing conditions etc., the catalytic performance of a given material can be predicted.…”

Assessment of Predictive Ability of Artificial Neural Networks Using Holographic Mapping

“…ANNs are excellent for non-linear regressions. Their pattern recognition and predictive ability make them especially beneficial in different optimization tasks, such as optimization of heterogeneous catalysts libraries [1][2][3][4][5][6][7]. Based on input variables, such as composition, preparation and testing conditions etc., the catalytic performance of a given material can be predicted.…”

Deep Learning Applications

“…The results obtained were compared with the predictions of a multiple linear regression model and it was shown that the neural network is superior to multiple linear regression model to predict the log CMC of anionic and cationic surfactants. Along the same line, Kardanpour et al [36] reported a wavelet neural network (WNN) to predict the critical micellar concentration of Gemini surfactants. The developed model used twelve different descriptors from the molecular structure.…”

High‐Throughput Heterogeneous Catalytic Science

“…Other approaches in guiding high‐throughput experimentation include the use of artificial neural networks51 and the holographic research strategy 52. A novel approach incorporating factor based methods from chemometrics,53 that is, principle component analysis (PCA) was proposed to initially decrease the dimensionality of the experimental parameter space 44.…”

Assessment of Predictive Ability of Artificial Neural Networks Using Holographic Mapping

“…ANNs are excellent for non-linear regressions. Their pattern recognition and predictive ability make them especially beneficial in different optimization tasks, such as optimization of heterogeneous catalysts libraries [1][2][3][4][5][6][7]. Based on input variables, such as composition, preparation and testing conditions etc., the catalytic performance of a given material can be predicted.…”

Deep Learning Applications

“…The results obtained were compared with the predictions of a multiple linear regression model and it was shown that the neural network is superior to multiple linear regression model to predict the log CMC of anionic and cationic surfactants. Along the same line, Kardanpour et al [36] reported a wavelet neural network (WNN) to predict the critical micellar concentration of Gemini surfactants. The developed model used twelve different descriptors from the molecular structure.…”

High‐Throughput Heterogeneous Catalytic Science

“…Other approaches in guiding high‐throughput experimentation include the use of artificial neural networks51 and the holographic research strategy 52. A novel approach incorporating factor based methods from chemometrics,53 that is, principle component analysis (PCA) was proposed to initially decrease the dimensionality of the experimental parameter space 44.…”

Assessment of Predictive Ability of Artificial Neural Networks Using Holographic Mapping

“…ANNs are excellent for non-linear regressions. Their pattern recognition and predictive ability make them especially beneficial in different optimization tasks, such as optimization of heterogeneous catalysts libraries [1][2][3][4][5][6][7]. Based on input variables, such as composition, preparation and testing conditions etc., the catalytic performance of a given material can be predicted.…”

Deep Learning Applications

“…The results obtained were compared with the predictions of a multiple linear regression model and it was shown that the neural network is superior to multiple linear regression model to predict the log CMC of anionic and cationic surfactants. Along the same line, Kardanpour et al [36] reported a wavelet neural network (WNN) to predict the critical micellar concentration of Gemini surfactants. The developed model used twelve different descriptors from the molecular structure.…”

High‐Throughput Heterogeneous Catalytic Science

“…Other approaches in guiding high‐throughput experimentation include the use of artificial neural networks51 and the holographic research strategy 52. A novel approach incorporating factor based methods from chemometrics,53 that is, principle component analysis (PCA) was proposed to initially decrease the dimensionality of the experimental parameter space 44.…”