Geometry optimization of acetonitrile monomer and dimers using CNDO/force method

1986 ◽  
Vol 96 (3-4) ◽  
pp. 285-290 ◽  
Author(s):  
R Brakaspathy ◽  
Surjit Singh
Keyword(s):  
Geometry Optimization ◽  
Force Method

GEOMETRY OPTIMIZATION OF AIR-ASSISTED SWIRL NOZZLE BASED ON SURROGATE MODELS AND COMPUTATIONAL FLUID DYNAMICS

2019 ◽  
Vol 29 (7) ◽  
pp. 605-628
Author(s):  
Zongli Yi ◽  
Li Hou ◽  
Qi Zhang ◽  
Yousheng Wang ◽  
Yunxia You
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Geometry Optimization ◽  
Surrogate Models

CLB18: A New Structural Database with Unusual Carbon–Carbon Long Bonds

2020 ◽  
Author(s):  
Pierpaolo Morgante ◽  
Roberto Peverati
Keyword(s):  
Transition Metal Complexes ◽  
Low Cost ◽  
Geometry Optimization ◽  
Structure Calculation ◽  
Unique Property ◽  
Spin States ◽  
Calculation Methods ◽  
Large Molecules ◽  
Barrier Heights ◽  
Structural Database

<div><div><div><p>In this Letter, we introduce a new database called carbon long bond 18 (CLB18), composed of 18 structures with one long C–C bond. We use this new database to evaluate the performance of several low-cost methods commonly used for geometry optimization of medium and large molecules. We found that the long bonds in CLB18 are electronically different from those found in barrier heights databases. We also report the unexpected correlation between the results of CLB18 and those of the energetics of spin states in transition-metal complexes. Given this unique property, CLB18 can be a useful tool for assessing existing electronic structure calculation methods and developing new ones.</p></div></div></div>

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Lead Molecules as Novel Aromatase Inhibitors: In Silico De Novo Designing and Binding Affinity Studies

2020 ◽  
Vol 17 (5) ◽  
pp. 655-665 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Drug Design ◽  
Aromatase Inhibitors ◽  
De Novo ◽  
Scale Up ◽  
Geometry Optimization ◽  
Docking Study ◽  
Molecular Docking Study ◽  
De Novo Drug Design ◽  
Molegro Virtual Docker ◽  
Estrogen Production

Background:Aromatase inhibitors emerged as a pivotal moiety to selectively block estrogen production, prevention and treatment of tumour growth in breast cancer. De novo drug design is an alternative approach to blind virtual screening for successful designing of the novel molecule against various therapeutic targets.Objective:In the present study, we have explored the de novo approach to design novel aromatase inhibitors.Method:The e-LEA3D, a computational-aided drug design web server was used to design novel drug-like candidates against the target aromatase. For drug-likeness ADME parameters (molecular weight, H-bond acceptors, H-bond donors, LogP and number of rotatable bonds) of designed molecules were calculated in TSAR software package, geometry optimization and energy minimization was accomplished using Chem Office. Further, molecular docking study was performed in Molegro Virtual Docker (MVD).Results:Among 17 generated molecules using the de novo pathway, 13 molecules passed the Lipinski filter pertaining to their bioavailability characteristics. De novo designed molecules with drug-likeness were further docked into the mapped active site of aromatase to scale up their affinity and binding fitness with the target. Among de novo fabricated drug like candidates (1-13), two molecules (5, 6) exhibited higher affinity with aromatase in terms of MolDock score (-150.650, -172.680 Kcal/mol, respectively) while molecule 8 showed lowest target affinity (-85.588 Kcal/mol).Conclusion:The binding patterns of lead molecules (5, 6) could be used as a pharmacophore for medicinal chemists to explore these molecules for their aromatase inhibitory potential.

Ion solvation by dipolar aprotic solvents. An ab initio study

1987 ◽  
Vol 52 (1) ◽  
pp. 6-13 ◽  
Author(s):  
Petr Kyselka ◽  
Zdeněk Havlas ◽  
Ivo Sláma
Keyword(s):  
Ab Initio ◽  
Geometry Optimization ◽  
Molecular Structures ◽  
Dimethyl Sulphoxide ◽  
Ion Solvation ◽  
Interaction Energies ◽  
Ab Initio Study ◽  
Solvation Energies ◽  
Dipolar Aprotic Solvents ◽  
Complete Geometry Optimization

The paper deals with the solvation of Li+, Be2+, Na+, Mg2+, and Al3+ ions in dimethyl sulphoxide, dimethylformamide, acetonitrile, and water. The ab initio quantum chemical method was used to calculate the solvation energies, molecular structures, and charge distributions for the complexes water···ion, acetonitrile···ion, dimethyl sulphoxide···ion, and dimethylformamide···ion. The interaction energies were corrected for the superposition error. Complete geometry optimization was performed for the complex water···ion. Some generalizations are made on the basis of the results obtained.

The minimum zone fitting and error evaluation for the logarithmic curve based on geometry optimization approximation algorithm

2019 ◽  
Vol 41 (15) ◽  
pp. 4380-4386
Author(s):  
Tu Xianping ◽  
Lei Xianqing ◽  
Ma Wensuo ◽  
Wang Xiaoyi ◽  
Hu Luqing ◽  
...  
Keyword(s):  
Approximation Algorithm ◽  
Evaluation Method ◽  
Geometry Optimization ◽  
Reference Points ◽  
Approximation Technique ◽  
Error Evaluation ◽  
Iterative Approximation ◽  
Logarithmic Curve ◽  
Normal Distance ◽  
Minimum Zone

The minimum zone fitting and error evaluation for the logarithmic curve has important applications. Based on geometry optimization approximation algorithm whilst considering geometric characteristics of logarithmic curves, a new fitting and error evaluation method for the logarithmic curve is presented. To this end, two feature points, to serve as reference, are chosen either from those located on the least squares logarithmic curve or from amongst measurement points. Four auxiliary points surrounding each of the two reference points are then arranged to resemble vertices of a square. Subsequently, based on these auxiliary points, a series of auxiliary logarithmic curves (16 curves) are constructed, and the normal distance and corresponding range of values between each measurement point and all auxiliary logarithmic curves are calculated. Finally, by means of an iterative approximation technique consisting of comparing, evaluating, and changing reference points; determining new auxiliary points; and constructing corresponding auxiliary logarithmic curves, minimum zone fitting and evaluation of logarithmic curve profile errors are implemented. The example results show that the logarithmic curve can be fitted, and its profile error can be evaluated effectively and precisely using the presented method.

scholarly journals Computational Study of the Electron Spectra of Vapor-Phase Indole and Four Azaindoles

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1947
Author(s):  
Delano P. Chong
Keyword(s):  
Density Functional Theory ◽  
Vapor Phase ◽  
Density Functional ◽  
Computational Study ◽  
Geometry Optimization ◽  
The Other ◽  
Functional Theory ◽  
Electron Spectra

After geometry optimization, the electron spectra of indole and four azaindoles are calculated by density functional theory. Available experimental photoemission and excitation data for indole and 7-azaindole are used to compare with the theoretical values. The results for the other azaindoles are presented as predictions to help the interpretation of experimental spectra when they become available.

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