Comparison of Machine Learning Algorithms in Screening Potential Additives to Ni/Al₂O₃ Methanation Catalysts for Improving the Anti‐Coking Performance

Abstract

In this paper, the 16 physicochemical properties of 56 elements were processed through principal component analysis (PCA) transformation and Gaussian mixture model clustering. And then, a pool of eleven representative elements was chosen for subsequent experiments on resistance to carbon deposition. Based on the experimental results and the principal components of the selected elements, radial basis function network (RBFN), support vector machine (SVM) and Gaussian process regression (GPR) models were constructed, respectively. Compared with other models, the prediction results of GPR model are more accurate. It predicted that W element is the most effective additive, which was confirmed by further experiments. To our knowledge, this finding has not yet been formally reported in the literature. The application and implementation of this strategy provides new ideas for high dimensional, small sample and nonlinear catalyst data modeling.

Quantum-Mechanical Transition-State Model Combined with Machine Learning Provides Catalyst Design Features for Selective Cr Olefin Oligomerization

“…While machine learning is beginning to find significant application as a tool to aid organic synthesis, 63,64,73,[65][66][67][68][69][70][71][72] there are relatively few examples of machine learning applied to inorganic or organometallic reactions, especially heterogeneous 45,74,[83][84][85][86][75][76][77][78][79][80][81][82] and homogeneous catalysis. 87,88 Recently, Kulik trained an artificial neural network to predict the high-spin to low-spin splitting energies of ~2700 transition metal complexes.…”

Quantum-Mechanical Transition-State Model Combined with Machine Learning Provides Catalyst Design Features for Selective Cr Olefin Oligomerization

“…While machine learning is beginning to find significant application as a tool to aid organic synthesis, 63,64,73,[65][66][67][68][69][70][71][72] there are relatively few examples of machine learning applied to inorganic or organometallic reactions, especially heterogeneous 45,74,[83][84][85][86][75][76][77][78][79][80][81][82] and homogeneous catalysis. 87,88 Recently, Kulik trained an artificial neural network to predict the high-spin to low-spin splitting energies of ~2700 transition metal complexes.…”