Vibrational Spectral Studies, Quantum Mechanical Properties, and Biological Activity Prediction and Inclusion Molecular Self-Assembly Formation of N-N’-Dimethylethylene Urea

A cyclic urea analog, N-N'-dimethylethylene urea, was studied using different spectral methods like FT-IR, FT-Raman, and UV-VIS methods followed by computational simulations. The experimental and simulated spectra are compared, and a detailed assignment of vibrations and potential energy distribution is made. It was followed by various quantum mechanical studies like frontier orbital analysis, energy descriptors, average local ionization energies, and nonlinear optical properties. The NBO gave an insight into the various intramolecular stabilizing electron delocalization by hyperconjugation. Noncovalent interaction analysis provided various types of interactions present in the molecule. We also studied ALIE, local information entropy, electron localized function, reduced density gradient studies, localized orbital locator studies, and other analyses. Molecular docking results indicated that this urea derivative acted as an ATP-hydrolysing inhibitor, and the drug delivery ability of cyclodextrin on NND was tested by forming an inclusion complex with both compounds with dispersion and without dispersion interaction.

3D Dense Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions

<p>Deep learning methods provide a novel way to establish a correlation between two quantities. In this context, computer vision techniques like 3D-Convolutional Neural Networks (3D-CNN) become a natural choice to associate a molecular property with its structure due to the inherent three-dimensional nature of a molecule. However, traditional 3D input data structures are intrinsically sparse in nature, which tend to induce instabilities during the learning process, which in turn may lead to under-fitted results. To address this deficiency, in this project, we propose to use quantum-chemically derived molecular topological features, namely, Localized Orbital Locator (LOL) and Electron Localization Function (ELF), as molecular descriptors, which provide a relatively denser input representation in three-dimensional space. Such topological features provide a detailed picture of the atomic configuration and inter-atomic interactions in the molecule and are thus ideal for predicting properties that are highly dependent on molecular geometry. Herein, we demonstrate the efficacy of our proposed model by applying it to the task of predicting atomization energies for the QM9-G4MP2 dataset, which contains ~134-k molecules. Furthermore, we incorporated the Δ-ML approach into our model, allowing us to reach beyond benchmark accuracy levels (~1.0 kJ mol⁻¹).We consistently obtain impressive MAEs of the order 0.1 kcal mol⁻¹ (~ 0.42 kJ mol⁻¹) <i>versus</i> G4(MP2) theory using relatively modest models, which could potentially be improved further using additional compute resources.</p>

3D Dense Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions

<p>Deep learning methods provide a novel way to establish a correlation between two quantities. In this context, computer vision techniques like 3D-Convolutional Neural Networks (3D-CNN) become a natural choice to associate a molecular property with its structure due to the inherent three-dimensional nature of a molecule. However, traditional 3D input data structures are intrinsically sparse in nature, which tend to induce instabilities during the learning process, which in turn may lead to under-fitted results. To address this deficiency, in this project, we propose to use quantum-chemically derived molecular topological features, namely, Localized Orbital Locator (LOL) and Electron Localization Function (ELF), as molecular descriptors, which provide a relatively denser input representation in three-dimensional space. Such topological features provide a detailed picture of the atomic configuration and inter-atomic interactions in the molecule and are thus ideal for predicting properties that are highly dependent on molecular geometry. Herein, we demonstrate the efficacy of our proposed model by applying it to the task of predicting atomization energies for the QM9-G4MP2 dataset, which contains ~134-k molecules. Furthermore, we incorporated the Δ-ML approach into our model, allowing us to reach beyond benchmark accuracy levels (~1.0 kJ mol⁻¹).We consistently obtain impressive MAEs of the order 0.1 kcal mol⁻¹ (~ 0.42 kJ mol⁻¹) <i>versus</i> G4(MP2) theory using relatively modest models, which could potentially be improved further using additional compute resources.</p>

Ellipticity, PDI and FLU, Evaluation of Aromaticity Indexes During Substituting B &N Atoms in Poly-Annulene

A close relationship between chemical shift and magnetic criteria for aromaticity arouses a deeper view for probing and modeling of induced current density in π systems through external magnetic fields. The (4n+2)π systems aromatic are studied on variants of Azabora Derivatives of [8] Annulene (BnNnC(8-2n)H8) via the localized orbital localization (LOL) and electron localized function (ELF) by considering the density functional calculation. By this work, it has been predicted a four-electron dia-tropic (aromatic) ring current for (4n+2) π variants of Bn NnC(8-2n) H8 and a two-electron para-tropic (anti-aromatic) current for (4n) π. With the HOMO and LUMO energies and also HOMO/LUMO overlapping in whole space, it is possible to predict the transition states from delocalized to-localized currents in all variant mentioned compounds in the viewpoint of aromaticity and anti-aromaticity. In addition, the nucleus independent chemical shifts (NICS), HOMA, Ellipticity, Aromatic Fluctuation index (FLU), and para delocalization index (PDI) values confirm the amounts of aromaticity and anti-aromaticity in those rings.

Substituent effects on the structure and properties of (para-C5H4X)Ir(PH3)3 complexes in the ground state (S0) and first singlet excited state (S1): DFT and TD-DFT investigations

The ground and lowest singlet excited state geometries of selected ( para-C5H4X)Ir(PH3)3 iridabenzene complexes ( para-substituent = NH2, OMe, Me, H, F, Cl, CCl3, CF3, NO2) are optimized using the MPW1PW91 procedure employing the LanL2DZ(Ir) and 6-311G(d, p) (C, H, N, O, P, F, Cl, P) basis sets. The excited state is generated using the time-dependent density function method. The effects of electron-donating groups and electron-withdrawing groups on the energy, atomization energy, rotational constants, and frontier orbital energies in the first singlet excited state (S1) of iridabenzene are investigated and compared to those of the ground state (S0). The Ir–C and Ir–P bonds in the studied molecules are analyzed by electron localization function and localized-orbital locator methods. The correlations between the Ir-C and Ir–P bond distances, electron localization function, and localized-orbital locator values Hammett constants (σp) and dual parameters (σI and σR) are given for the two studied states. The para-delocalization index is used for investigation of the aromaticity of the studied complexes.