Machine Learning-Aided Structure Determination for TiCl4–Capped MgCl2 Nanoplate of Heterogeneous Ziegler–Natta Catalyst

Abstract

Dynamic reconstruction under a physicochemical environment is an intrinsic property of solid surfaces, in particular when associated with catalysis on nanosized or nanostructured materials. Here, we report nonempirical structure determination of TiCl4-capped MgCl2 nanoplates that is based on the combination of a genetic algorithm and density functional calculations. The methodology for the nonempirical structure determination was developed, and its application was demonstrated for 7MgCl2, 15MgCl2, and 15MgCl2/4TiCl4 in relation to the hidden identity of primary particles of the Ziegler–Natta catalyst. Bare MgCl2 nanoplates dominantly exposed {100} surfaces at their lateral cuts, but the chemisorption of TiCl4 induced reconstruction by stabilizing {110} surfaces. The most important finding of the present research is that TiCl4 exhibited distributed adsorption states as consequences of chemisorption on nonideal finite surfaces and the diversity of thermodynamically accessible structures. The assessment of the Ti distribution is essential for the distribution of primary structures of produced polymer, and in this study, we made these determinations.