scholarly journals Computational Exploration of Functionalized Rhombellanes: Building Blocks and Double-Shell Structures

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 343 ◽  
Author(s):  
Katalin Nagy ◽  
Beata Szefler ◽  
Csaba L. Nagy
Keyword(s):  
Density Functional ◽  
Molecular Design ◽  
Drug Carrier ◽  
Geometry Optimization ◽  
Molecular Geometry ◽  
Building Blocks ◽  
Great Promise ◽  
Molecular Symmetry ◽  
Guest Molecules ◽  
Computational Exploration

Double-shell covalent assemblies with the framework of the cube–rhombellane were recently proposed as potential drug delivery systems. Their potential to encapsulate guest molecules combined with appropriate surface modifications show great promise to meet the prerequisites of a drug carrier. This work reports the molecular design of such clusters with high molecular symmetry, as well as the evaluation of the geometric and electronic properties using density functional theory. The computational studies of the double-shell assemblies and their corresponding building blocks were conducted using the B3LYP/6-31G(d,p) method as implemented in Gaussian 09. The results show that the assembly of the building blocks is energetically favorable, leading to clusters with higher stability than the corresponding shell fragments, with large HOMO–LUMO gap values. In case of aromatic systems, interlayer stacking interactions between benzene rings contribute to the molecular geometry and stability. During geometry optimization the clusters preserve the high molecular symmetry of the building blocks.

scholarly journals Contrasting Photo-Switching Rates in Azobenzene Derivatives: How the Nature of the Substituent Plays a Role

Polymers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1019
Author(s):  
Domenico Pirone ◽  
Nuno A. G. Bandeira ◽  
Bartosz Tylkowski ◽  
Emily Boswell ◽  
Regine Labeque ◽  
...  
Keyword(s):  
Visible Light ◽  
Smart Materials ◽  
Density Functional ◽  
Molecular Design ◽  
Visible Region ◽  
Building Blocks ◽  
Stimuli Responsive ◽  
Order Of Magnitude ◽  
Azo Derivatives ◽  
Commercial Polymers

A molecular design approach was used to create asymmetrical visible light-triggered azo-derivatives that can be good candidates for polymer functionalization. The specific electron–donor substituted molecules were characterized and studied by means of NMR analyses and UV-visible spectroscopy, comparing the results with Time Dependent Density Functional (TD-DFT) calculations. A slow rate of isomerization (ki = 1.5 × 10−4 s−1) was discovered for 4-((2-hydroxy-5methylphenyl) diazenyl)-3-methoxybenzoic acid (AZO1). By methylating this moiety, it was possible to unlock the isomerization mechanism for the second molecule, methyl 3-methoxy-4-((2-methoxy-5-methylphenyl) diazenyl)benzoate (AZO2), reaching promising isomerization rates with visible light irradiation in different solvents. It was discovered that this rate was heightened by one order of magnitude (ki = 3.1 × 10−3 s−1) for AZO2. A computational analysis using density functional (DFT/PBE0) and wavefunction (QD-NEVPT2) methodologies provided insight into the photodynamics of these systems. Both molecules require excitation to the second (S2) excited state situated in the visible region to initiate the isomerization. Two classic mechanisms were considered, namely rotation and inversion, with the former being energetically more favorable. These azo-derivatives show potential that paves the way for future applications as building blocks of functional polymers. Likewise, they could be really effective for the modification of existing commercial polymers, thus transferring their stimuli responsive properties to polymeric bulky structures, converting them into smart materials.

scholarly journals Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging

2020 ◽  
Author(s):  
Gerardo Raggi ◽  
Ignacio Fernández Galván ◽  
Christian L. Ritterhoff ◽  
Morgane Vacher ◽  
Roland Lindh
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Geometry Optimization ◽  
Arbitrary Point ◽  
Molecular Geometry ◽  
Second Order ◽  
Machine Learning Techniques ◽  
Stationary Points ◽  
Molecular Geometry Optimization ◽  
Test Suites

Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional - step-restricted second-order truncated expansion - molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In the paper we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect the GEK procedure will be used to mimic the behavior of a conventional second-order scheme, but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimization to facilitate restricted-variance optimizations (RVO). A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two - the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, the full Baker-TS, and the S22 test suites, at the density-functional-theory and second order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected.

scholarly journals Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging

2020 ◽  
Author(s):  
Gerardo Raggi ◽  
Christian L. Ritterhoff ◽  
Ignacio Fernández Galván ◽  
Morgane Vacher ◽  
Roland Lindh
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Geometry Optimization ◽  
Arbitrary Point ◽  
Molecular Geometry ◽  
Second Order ◽  
Machine Learning Techniques ◽  
Stationary Points ◽  
Molecular Geometry Optimization ◽  
Test Suites

Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional - step-restricted second-order truncated expansion - molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In the paper we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect the GEK procedure will be used to mimic the behavior of a conventional second-order scheme, but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimization to facilitate restricted-variance optimizations (RVO). A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two - the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, the full Baker-TS, and the S22 test suites, at the density-functional-theory and second order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected.

Electron Dynamics of Solvated Ti(OH)4

2014 ◽  
Vol 1647 ◽  
Author(s):  
Dayton J. Vogel ◽  
Dmitri S. Kilin
Keyword(s):  
Density Functional ◽  
Computational Study ◽  
Geometry Optimization ◽  
Building Blocks ◽  
Basis Set ◽  
Synthesis Methods ◽  
Electron Dynamics ◽  
Relaxation Rates ◽  
Electron Hole ◽  
Ambient Temperatures

ABSTRACTIncreasing interest in the photocatalytic activity of TiO2 has led to considerations of using TiO2 nanoparticles in energy generation. In order to better understand the electron-hole relaxation of nano scale TiO2 structures, it is important to start with an understanding of TiO2 synthesis building blocks. The solvated titanium (IV) ion is a precursor found in synthesis methods of colloidal TiO2 nanostructures. This simplest test compound may reflect some common basic electronic features for larger structures composed of Ti(IV) coordinated with oxygen. For this computational study, a model of Ti(OH)4 with tetrahedral coordination was created. To simulate the electronic properties of a solution of Ti(IV), the model was surrounded with 27 H2O molecules. The model was explored by means of standard density functional theory (DFT) molecular dynamics (MD) followed by nonadiabatic electron dynamics computed with Reduced Density Matrix approach combined with “on-the-fly coupling”. Results were generated with Vienna ab initio Simulation Package (VASP) using the Perdew-Burke-Ernzerhof (PBE) functional, plane wave basis set, and projector augmented wave (PAW) potentials. The absorption spectra, MD, and electron-hole relaxation rates are presented for the Ti(OH)4 model at various ambient temperatures. The electron-hole relaxation rates show a non-linear dependence on temperature and were found to be near the same order of magnitude as electron-hole relaxation rates in bulk TiO2 calculations. A video of the geometry optimization can be found online.[1]

Crystal structure of 1-propanethiol–Co2(dobdc) from laboratory X-ray powder diffraction data

2020 ◽  
Vol 35 (1) ◽  
pp. 3-6
Author(s):  
Jonathan B. Lefton ◽  
Kyle B. Pekar ◽  
Daniel Sethio ◽  
Elfi Kraka ◽  
Tomče Runčevski
Keyword(s):  
Crystal Structure ◽  
Powder Diffraction ◽  
Density Functional ◽  
Weak Interactions ◽  
Geometry Optimization ◽  
Cobalt Metal ◽  
Guest Molecules ◽  
X Ray ◽  
Cell Parameters ◽  
Alkane Chain

Laboratory X-ray powder diffraction was used to solve and refine the crystal structures of appended guest molecules within the pores of metal–organic frameworks (MOFs). Herein, we report the crystal structure of 1-propanethiol adsorbed in the pores of Co2(dobdc) (dobdc4– = 2,5-dioxido-1,4-benzenedicarboxylate, MOF-74). Soaking the activated MOF in neat 1-propanethiol resulted in the formation of 1-propanethiol–Co2(dobdc). The thiol appendant MOF maintained the crystal symmetry, with a rhombohedral space group R-3 and unit-cell parameters a = 25.9597(9) Å, c = 6.8623(5) Å, and V = 4005.0(4) Å3. As expected, the thiol sulfur formed a bond with the open cobalt metal site. The alkane chain was directed toward the center of the pore, participating in numerous van der Waals weak interactions with neighboring molecules. For the final Rietveld refinement, soft restrains were applied using bond distances obtained by periodic density functional theory (DFT) geometry optimization.

scholarly journals Comparison of Molecular Geometry Optimization Methods Based on Molecular Descriptors

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2855
Author(s):  
Donatella Bálint ◽  
Lorentz Jäntschi
Keyword(s):  
Density Functional ◽  
Geometry Optimization ◽  
Principal Component ◽  
Optimization Procedure ◽  
Molecular Geometry ◽  
Optimization Methods ◽  
Hartree Fock ◽  
Pubchem Database ◽  
Semi Empirical ◽  
The Relationship

Various methods (Hartree–Fock methods, semi-empirical methods, Density Functional Theory, Molecular Mechanics) used to optimize a molecule structure feature the same basic approach but differ in the mathematical approximations used. The geometry optimization procedure calculates the energy at an initial geometry of a molecule and then proceeds to search a new geometry with a lower energy. Using the 3D structures collected from the PubChem database, 20 amino acid geometry optimization calculations were performed with several methods. The purpose of the study was to analyze these methods (39) to find the relationship between them and to determine which to use under different circumstances. Cluster analysis and principal component analysis were performed to evaluate the similarities between the different methods. The results after the analysis can classified into three main groups and can be selected accordingly to solve different types of problems.

scholarly journals Synthesis, Characterization and Theoretical Studies of New Organotellurium Compounds Based on (4-(((1S,E)-1,7,7-trimethylbicyclo[2.2.1]heptan-2-ylidene)amino)phenyl)mercury(II) Chloride

2021 ◽  
Vol 21 (6) ◽  
pp. 1443
Author(s):  
Nuha Hussain Al-Saadawy
Keyword(s):  
Density Functional ◽  
Energy Gap ◽  
Condensation Reaction ◽  
Geometry Optimization ◽  
Molecular Geometry ◽  
Basis Set ◽  
Chemical Structures ◽  
Lower Case ◽  
Donor And Acceptor ◽  
Organotellurium Compounds

The current study aimed to prepare new organomercury and organotellurium compounds based on the condensation reaction of 1,7,7-trimethylbicyclo[2.2.1]heptan-2-one (camphor) and p-aminophenyl mercuric(II) chloride. All the prepared compounds were characterized using different methods such as infrared spectrum, nuclear magnetic resonance, and CHN analysis. The analysis results concurred with the suggested chemical structures of the prepared compounds. Density functional theory has been applied with the basis set 3-21G to investigate the molecular structure of the prepared organotellurium compounds. Geometrical structure, HOMO surfaces, LUMO surfaces, and energy gap have been produced throughout the geometry optimization. The molecular geometry and contours for organotellurium compounds have been investigated throughout the geometrical optimization. Also, the donor and acceptor have been studied by comparing the HOMO energies of the prepared organotellurium compounds. Finally, the electronegativity, electrophilicity, ionization potential, electron affinity, and lower case of organotellurium compounds have been calculated and discussed.

scholarly journals Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging

2020 ◽  
Author(s):  
Gerardo Raggi ◽  
Ignacio Fernández Galván ◽  
Christian L. Ritterhoff ◽  
Morgane Vacher ◽  
Roland Lindh
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Geometry Optimization ◽  
Arbitrary Point ◽  
Molecular Geometry ◽  
Second Order ◽  
Machine Learning Techniques ◽  
Stationary Points ◽  
Molecular Geometry Optimization ◽  
Test Suites

Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional - step-restricted second-order truncated expansion - molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In the paper we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect the GEK procedure will be used to mimic the behavior of a conventional second-order scheme, but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimization to facilitate restricted-variance optimizations (RVO). A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two - the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, the full Baker-TS, and the S22 test suites, at the density-functional-theory and second order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected.

scholarly journals DENSITY FUNCTIONAL THEORY INVESTIGATION FOR INSB NANOCRYSTAL DIAMANTANE DRUG CARRIER

2021 ◽  
Vol 03 (02) ◽  
pp. 18-27
Author(s):  
Methaq Talib MATROOD ◽  
Aqeel Adil HASAN
Keyword(s):  
Density Functional Theory ◽  
Molecular Orbital ◽  
Density Functional ◽  
Drug Carrier ◽  
National Level ◽  
Geometry Optimization ◽  
Basis Sets ◽  
Functional Theory ◽  
Highest Occupied Molecular Orbital ◽  
Lowest Unoccupied Molecular Orbital

Nanomedicine remains the medicinalrequest of nanotechnology. Nanodrugvarietiesafter the medicinalrequests of nanoparticles, to nanoelectronic biosensors, thenuniform possible future applications of particle nanotechnology.Nanoparticle of medicationtransporters are optimized aimed atpreoccupation of medicationsfinishedbreathtreatment. Demonstrating and imitation of nanocrystal limits of the theophylline (C7H8N4O2)byindium – antimony (In7Sb7H20 (in diamantane constructionhave been performed by Gaussian 09 program. DFT hasremainedused for InSb nanoparticle, theophyllinemedication. Optimization and frequency on the ground national level,PBEPBE, 3-21G basis sets consumesremainedexamined. The custodiesaimed ataltogetherremainequivalenttoward zero custodies. The geometry optimization by means of both methods (PBE) for InSb diamantane nanoparticles and theophyllinedrug has been originate cutting-edgedecent agreement by experimental dataMolecular detour theory has been used to discovery highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies. Total energy, ionization potential and electron empathy have beenintendedaimed atInSbnanostructure bytheophyllinemedication.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

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