Unveiling the Nature and Strength of Selenium-Centered Chalcogen Bonds in Binary Complexes of SeO2 with Oxygen-/Sulfur-Containing Lewis Bases: Insights from Theoretical Calculations.

Among various non-covalent interactions, selenium-centered chalcogen bonds (SeChBs) have garnered considerable attention in recent years as a result of their important contributions to crystal engineering, organocatalysis, molecular recognition, materials science, and biological systems. Herein, we systematically investigated π-hole-type Se∙∙∙O/S ChBs in the binary complexes of SeO2 with a series of O-/S-containing Lewis bases by means of high-level ab initio computations. The results demonstrate that there exists an attractive interaction between the Se atom of SeO2 and the O/S atom of Lewis bases. The interaction energies computed at the MP2/aug-cc-pVTZ level range from -4.68 kcal/mol to -10.83 kcal/mol for the Se∙∙∙O chalcogen-bonded complexes and vary between -3.53 kcal/mol and -13.77 kcal/mol for the Se∙∙∙S chalcogen-bonded complexes. The Se∙∙∙O/S ChBs exhibit a relatively short binding distance in comparison to the sum of the van der Waals radii of two chalcogen atoms. The Se∙∙∙O/S ChBs in all of the studied complexes show significant strength and a closed-shell nature, with a partially covalent character in most cases. Furthermore, the strength of these Se∙∙∙O/S ChBs generally surpasses that of the C/O-H∙∙∙O hydrogen bonds within the same complex. It should be noted that additional C/O-H∙∙∙O interactions have a large effect on the geometric structures and strength of Se∙∙∙O/S ChBs. Two subunits are connected together mainly via the orbital interaction between the lone pair of O/S atoms in the Lewis bases and the BD*(OSe) anti-bonding orbital of SeO2, except for the SeO2∙∙∙HCSOH complex. The electrostatic component emerges as the largest attractive contributor for stabilizing the examined complexes, with significant contributions from induction and dispersion components as well.

Computational Insight into the Nature and Strength of the π-Hole Type Chalcogen∙∙∙Chalcogen Interactions in the XO2∙∙∙CH3YCH3 Complexes (X = S, Se, Te; Y = O, S, Se, Te).

In recent years, the non-covalent interactions between chalcogen centers have aroused substantial research interest because of their potential applications in organocatalysis, materials science, drug design, biological systems, crystal engineering, and molecular recognition. However, studies on π-hole-type chalcogen∙∙∙chalcogen interactions are scarcely reported in the literature. Herein, the π-hole-type intermolecular chalcogen∙∙∙chalcogen interactions in the model complexes formed between XO2 (X = S, Se, Te) and CH3YCH3 (Y = O, S, Se, Te) were systematically studied by using quantum chemical computations. The model complexes are stabilized via one primary X∙∙∙Y chalcogen bond (ChB) and the secondary C-H∙∙∙O hydrogen bonds. The binding energies of the studied complexes are in the range of -21.6~-60.4 kJ/mol. The X∙∙∙Y distances are significantly smaller than the sum of the van der Waals radii of the corresponding two atoms. The X∙∙∙Y ChBs in all the studied complexes except for the SO2∙∙∙CH3OCH3 complex are strong in strength and display a partial covalent character revealed by conducting the quantum theory of atoms in molecules (QTAIM), a non-covalent interaction plot (NCIplot), and natural bond orbital (NBO) analyses. The symmetry-adapted perturbation theory (SAPT) analysis discloses that the X∙∙∙Y ChBs are primarily dominated by the electrostatic component.

Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials.

Crystalline borates are an important class of functional materials with wide applications in photocatalysis and laser technologies. Obtaining their band gap values in a timely and precise manner is a great challenge in material design due to the issues of computational accuracy and cost of first-principles methods. Although machine learning (ML) techniques have shown great successes in predicting the versatile properties of materials, their practicality is often limited by the data set quality. Here, by using a combination of natural language processing searches and domain knowledge, we built an experimental database of inorganic borates, including their chemical compositions, band gaps, and crystal structures. We performed graph network deep learning to predict the band gaps of borates with accuracy, and the results agreed favorably with experimental measurements from the visible-light to the deep-ultraviolet (DUV) region. For a realistic screening problem, our ML model could correctly identify most of the investigated DUV borates. Furthermore, the extrapolative ability of the model was validated against our newly synthesized borate crystal Ag3B6O10NO3, supplemented by the discussion of an ML-based material design for structural analogues. The applications and interpretability of the ML model were also evaluated extensively. Finally, we implemented a web-based application, which could be utilized conveniently in material engineering for the desired band gap. The philosophy behind this study is to use cost-effective data mining techniques to build high-quality ML models, which can provide useful clues for further material design.

Accelerating Discovery of Metal-Organic Frameworks for Methane Adsorption with Hierarchical Screening and Deep Learning.

In recent years, machine learning (ML) methods have made significant progress, and ML models have been adopted in virtually all aspects of chemistry. In this study, based on the crystal graph convolutional neural networks algorithm, an end-to-end deep learning model was developed for predicting the methane adsorption properties of metal-organic frameworks (MOFs). High-throughput grand canonical Monte Carlo calculations were carried out on the computation-ready, experimental MOF database, which contains approximately 11 000 MOFs, to construct the data set. An area under the curve of 0.930 for the test set proved the reliability of the developed deep learning model. To assess the transferability of the model, we applied it to predict the methane adsorption volume for some randomly selected covalent organic frameworks and zeolitic imidazolate framework materials. The results indicated that the model could also be suitable for other porous materials. We also applied it to the hierarchical screening of a hypothetical MOFs database (∼330 000 MOFs). Four hypothetical MOFs were demonstrated to have the highest performance in methane adsorption. A calculated maximum working capacity of 145 cm3/cm3 at 5-35 bar and 298 K indicated that the hypothetical MOF is close to the Department of Energy's 2015 target of 180 cm3/cm3. Further analyses on all screened out MOFs established correlations between some structural features with the working capacity. The successful incorporation of ML and hierarchical screening can accelerate the discovery of new materials not just for gas adsorption, but also other areas involving interactions in materials and molecules.

A Ti-MOF Decorated With a Pt Nanoparticle Cocatalyst for Efficient Photocatalytic H2 Evolution: A Theoretical Study.

Pt nanoparticles (NPs) are often used as cocatalysts to enhance the photocatalytic hydrogen production catalyzed by the metal organic framework (MOF) materials. The catalytic efficiency of many Pt/MOF systems can be greatly improved when Pt NPs are used as cocatalysts. In this work, the Pt/20%-MIL-125-(SCH3)2 was chosen as the template material to understand the functional role of a Pt metal cocatalyst in the catalytic process. Experimentally, the catalytic activity of Pt/20%-MIL-125-(SCH3)2 is more than 100 times that of the system without the help of Pt NPs. Firstly, we proposed a searching algorithm, which is based on the combined Monte Carlo (MC) method and principal component analysis (PCA) algorithm, to find that the most probable adsorption site of the Pt13 nanocluster loaded on the (001) surface of 20%-MIL-125-(SCH3)2. Next, by using density functional theory (DFT) and time-dependent density functional theory (TDDFT) methods, we revealed that the accumulation of some positive charges on the Pt13 cluster and proton adsorbed on the Pt13 cluster, which can promote the separation of photogenerated electrons and holes, thus improving the photocatalytic efficiency. This work not only provides a method to obtain the adsorption configuration of metal clusters on various MOFs but also provides a new insight into increasing photocatalytic efficiency for H2 production in Pt/MOF systems.

A 36-Membered Ring Metal Chalcogenide with a Very Low Framework Density.

Reported here is a new open-framework metal chalcogenide containing extra-large 36-ring channels. This compound has a 3-connected etc topology by regarding supertetrahedral T2 clusters as the structural nodes. It has a very low framework density (3.4 tetrahedra per 1000 Å3) with each framework cation participating in three 3-rings. The organic cations within its intersecting channels can be partially exchanged out by Cs+ ions with the preservation of its framework structure.