Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology.

Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel "composite" DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between "active" and "inactive" compounds.

DFT Study of WS2-Based Nanotubes Electronic Properties under Torsion Deformations.

In this study, the influence of torsional deformations on the properties of chiral WS2-based nanotubes was investigated. All calculations presented in this study were performed using the density functional theory (DFT) and atomic gaussian type orbitals basis set. Nanotubes with chirality indices (8, 2), (12, 3), (24, 6) and (36, 9) corresponding to diameters of 10.68 Å, 14.90 Å, 28.26 Å and 41.90 Å, respectively, are examined. Our results reveal that for nanotubes with smaller diameters, the structure obtained through rolling from a slab is not optimal and undergoes spontaneous deformation. Furthermore, this study demonstrates that the nanotube torsion deformation leads to a reduction in the band gap. This observation suggests the potential for utilizing such torsional deformations to enhance the photocatalytic activity of the nanotubes.

First-Principles Calculations of Phonons and Thermodynamic Properties of Zr(Hf)S2 -Based Nanotubes.

Comparative hybrid density functional calculations on the structure, stability, and phonon frequencies of monolayers and single-walled nanotubes are performed for Zr(Hf)S2 disulfides. The first-principles calculations of HfS2 -based nanotubes are made for the first time. The symmetry analysis of infrared and Raman active vibrational modes in ZrS2 and HfS2 nanotubes is made using the induced representations of the isogonal point groups of line groups. It is shown that the number of infrared and Raman active modes is constant for NTs with the same chirality type. The correlation of the phonon modes of the nanotubes of relatively large diameters with those of monolayer is analyzed. The thermodynamic functions of monolayers and nanotubes with various chirality and diameters are calculated on the basis of the obtained phonon frequencies. It is established that the phonon contribution to the nanotube strain energy is small, but may be important for an accurate estimate of the stability of the nanotubes of small diameters. The calculated results show that the thermal contributions to Helmholtz free energy are positive; thereby they slightly reduce the stability of ZrS2 and HfS2 nanotubes at elevated temperatures. © 2019 Wiley Periodicals, Inc.