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

Machine learning facilitating the rational design of nanozymes

As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics.

A combined experimental and molecular simulation study on stress generation phenomena during the Ziegler-Natta polyethylene catalyst fragmentation process

The morphology of particles obtained under different pre-polymerization conditions has been connected to the stress generation mechanism at the polymer/catalyst interface. A combination of experimental characterization techniques and atomistic molecular dynamics simulations allowed a systematic investigation of experimental conditions leading to a certain particle morphology, and hence to a final polymer with specific features. Atomistic models of nascent polymer phases in contact with magnesium dichloride surfaces have been developed and validated. Using these detailed models, in the framework of McKenna's hypothesis, the pressure increase due to the polymerization reaction has been calculated under different conditions and is in good agreement with experimental scenarios. This molecular scale knowledge and the proposed investigation strategy would allow the pre-polymerization conditions to be better defined and the properties of the nascent polymer to be tuned, ensuring proper operability along the whole polymer production process.

Preventing Premature Convergence in Evolutionary Structure Determination of Complex Molecular Systems: Demonstration in Few-Nanometer-Sized TiCl4-Capped MgCl2 Nanoplates

The combination of genetic algorithm-based global search and local geometry optimization enables nonempirical structure determination for complex materials such as practical solid catalysts. However, premature convergence in the genetic algorithm hinders the determination of the global minimum for complicated molecular systems. Here, we implemented a distributed genetic algorithm based on the migration from a structure database for avoiding the premature convergence, and thus we realized the structure determination for TiCl₄-capped MgCl₂ nanoplates with experimentally consistent sizes. The obtained molecular models are featured with a realistic size and nonideal surfaces, representing actual primary particles of catalysts.

Comparison of Catalysts with MIRA21 Model in Heterogeneous Catalytic Hydrogenation of Aromatic Nitro Compounds

The vast majority of research and development activities begins with a detailed literature search to explore the current state-of-the-art. However, this search becomes increasingly difficult as we go into the information revolution of 21st century. The aim of the work is to establish a functional and practical mathematical model of catalyst characterization and exact comparison of catalysts. This work outlines the operation of the MIskolc RAnking 21 (MIRA21) model through the reaction of nitrobenzene catalytic hydrogenation to aniline. A total of 154 catalysts from 45 research articles were selected, studied, characterized, ranked, and classified based on four classes of descriptors: catalyst performance, reaction conditions, catalyst conditions, and sustainability parameters. MIRA21 is able to increase the comparability of different types of catalysts and support catalyst development. According to the model, 8% of catalysts received D1 (top 10%) classification. This ranking model is able to show the most effective catalyst systems that are suitable for the production of aniline.

Effect of Internal Donors on Raman and IR Spectroscopic Fingerprints of MgCl2/TiCl4 Nanoclusters Determined by Machine Learning and DFT

To go deep into the origin of MgCl2 supported Ziegler-Natta catalysis we need to fully understand the structure and properties of precatalytic nanoclusters MgCl2/TiCl4 in presence of Lewis bases as internal donors (ID). In this work MgCl2/TiCl4 nanoplatelets derived by machine learning and DFT calculations have been used to model the interaction with ethyl-benzoate EB as ID, with available exposed sites of binary TixCly/MgCl2 systems. The influence of vicinal Ti2Cl8 and coadsorbed TiCl4 on energetic, structural and spectroscopic behaviour of EB has been considered. The adsorption of homogeneous-like TiCl4EB and TiCl4(EB)2 at the various surface sites have been also simulated. B3LYP-D2 and M06 functionals combined with TZVP quality basis set have been adopted for calculations. The adducts have been characterized by computing IR and Raman spectra that have been found to provide specific fingerprints useful to identify surface species; IR spectra have been successfully compared to available experimental data.

The atomic defects on the (104) and (110) surfaces of the MgCl2-supported Ziegler–Natta catalyst: a periodic DFT study

This work presents a DFT study on the effects of atomic defects in the MgCl2-supported Ziegler–Natta catalyst. The adsorption behaviours of TiCl4 and internal donors on the ideal and defective MgCl2(104) and (110) surfaces were investigated.

Dataset of energetically accessible structures of MgCl2/TiCl4 clusters for Ziegler-Natta catalysts

This data article provides a dataset of the energetically accessible structures including the most stable structures of xMgCl₂/yTiCl₄ nanoplates (x = 6-19, y = 0-4). TiCl₄-capped MgCl₂ nanoplates are regarded as the building block of the Ziegler-Natta catalyst. The most stable structures were determined for MgCl₂/TiCl₄ nanoplates of different sizes and chemical compositions using a combination of the genetic algorithm and the DFT geometry optimization. The evolution in the genetic algorithm produced a number of meta-stable structures. A set of isomeric structures having similar energy to the most stable structure (termed energetically accessible structures) are provided as realistic models of MgCl₂/TiCl₄ nanoplates. These structures are useful for further investigation on the structural distribution of Ti species on MgCl₂ regarding the Ziegler-Natta catalyst.

Structural Disorder of Mechanically Activated δ-MgCl2 Studied by Synchrotron X-ray Total Scattering and Vibrational Spectroscopy

A combination of synchrotron X-ray total scattering and molecular simulation is a powerful approach for reliable determination of the structure of δ-MgCl2 as an indispensable component of heterogeneous Ziegler–Natta catalysts. Here, the same approach is applied to mechanically activated MgCl2. Four types of mechanically activated MgCl2 samples are prepared using ball-milling in the absence and presence of different donors. The development of structural disorder along the grinding time is compared. It was found that the presence of donors accelerates the formation of δ-MgCl2 in an early stage of grinding, while elongated grinding eventually results in δ-MgCl2 with similar extents of structural disorder in the absence and presence of different donors. The FT-IR investigation consistently verified the morphological similarity between the firmly ground samples. Thus, the structure of δ-MgCl2 is likely governed by mechanical energy when sufficiently ground.

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.

NWPEsSe: An Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems

Global optimization constitutes an important and fundamental problem in theoretical studies in many chemical fields, such as catalysis, materials, or separations problems. In this paper, a novel algorithm has been developed for the global optimization of large systems including neat and ligated clusters in the gas phase and supported clusters in periodic boundary conditions. The method is based on an updated artificial bee colony (ABC) algorithm method, that allows for adaptive-learning during the search process. The new algorithm is tested against four classes of systems of diverse chemical nature: gas phase Au₅₅, ligated Au₈²⁺, Au₈ supported on graphene oxide and defected rutile, and a large cluster assembly [Co₆Te₈(PEt₃)₆][C₆₀]_n, with sizes ranging between 1 and 3 nm and containing up to 1300 atoms. Reliable global minima (GMs) are obtained for all cases, either confirming published data or reporting new lower energy structures. The algorithm and interface to other codes in the form of an independent program, Northwest Potential Energy Search Engine (NWPEsSe), is freely available, and it provides a powerful and efficient approach for global optimization of nanosized cluster systems.

Development of Large-Scale Stopped-Flow Technique and its Application in Elucidation of Initial Ziegler-Natta Olefin Polymerization Kinetics

The stopped-flow (SF) technique has been extensively applied to study Ziegler-Natta (ZN) olefin polymerization kinetics within an extremely short period (typically <0.2 s) for understanding the nature of the active sites as well as the polymerization mechanisms through microstructure analyses of obtained polymers. In spite of its great applicability, a small amount of polymer that is yielded in a short-time polymerization has been a major bottleneck for polymer characterizations. In order to overcome this limitation, a large-scale SF (LSF) system has been developed, which offers stable and scalable polymerization over an expanded time range from a few tens milliseconds to several seconds. The scalability of the LSF technique has been further improved by introducing a new quenching protocol. With these advantages, the LSF technique has been effectively applied to address several unknown issues in ZN catalysis, such as the role of physical and chemical transformations of a catalyst on the initial polymerization kinetics, and regiochemistry of ZN propylene polymerization. Here, we review the development of the LSF technique and recent efforts for understanding heterogeneous ZN olefin polymerization catalysis with this new system.