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MACHINE LEARNING AND STATISTICAL ANALYSIS FOR CATALYST STRUCTURE PREDICTION AND DESIGN
MACHINE LEARNING AND STATISTICAL ANALYSIS FOR CATALYST STRUCTURE PREDICTION AND DESIGN
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机译：催化剂结构预测与设计的机器学习与统计分析
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摘要
Disclosed is a heteroatomic ligand-metal compound complex transition-state model which has been developed for activity, purity, and/or selectivity for selective ethylene oligomerizations, and density functional theory calculations for determining heteroatomic ligand-metal compound complex reactivity, product purity, and/or selectivity for ethylene trimerizations and/or tetramerizations. Using reaction ground states and transition states, and/or reaction ground states and transition states in combination with the energetic span model, this disclosure reveals that in a chromium chromacycle mechanism, there are multiple ground states and multiple transition states, which can account for activity, purity, and/or selectivity for selective ethylene oligomerizations. Based on the reaction ground states and transition states, and/or reaction ground states and transition states in combination with the energetic span model, the methods disclosed herein can be used qualitatively and semi-quantitatively to predict relative heteroatomic ligand-metal compound complex activity, purity, and/or selectivity and lead to a successful process for catalyst design and implementation, in which new ligands can be successfully identified and experimentally validated.
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