Energy gradient in a multi-configurational SCF formalism and its application to geometry optimization of trimethylene diradicals

1979 ◽  
Vol 65 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Shigeki Kato ◽  
Keiji Morokuma
Keyword(s):  
Geometry Optimization ◽  
Energy Gradient

Analytic gradients, geometry optimization and excited state potential energy surfaces from the particle-particle random phase approximation

2015 ◽  
Vol 17 (2) ◽  
pp. 1025-1038 ◽  
Author(s):  
Du Zhang ◽  
Degao Peng ◽  
Peng Zhang ◽  
Weitao Yang
Keyword(s):  
Excited States ◽  
Potential Energy Surfaces ◽  
Random Phase Approximation ◽  
Theoretical Study ◽  
Random Phase ◽  
Geometry Optimization ◽  
Photochemical Reactions ◽  
Energy Gradient ◽  
State Potential ◽  
Energy Surfaces

The energy gradient for electronic excited states is of immense interest not only for spectroscopy but also for the theoretical study of photochemical reactions.

Geometry optimization of molecules in solution: Joint use of the mean field approximation and the free-energy gradient method

2003 ◽  
Vol 118 (1) ◽  
pp. 255-263 ◽  
Author(s):  
I. Fdez. Galván ◽  
M. L. Sánchez ◽  
M. E. Martín ◽  
F. J. Olivares del Valle ◽  
M. A. Aguilar
Keyword(s):  
Free Energy ◽  
Gradient Method ◽  
Mean Field ◽  
Geometry Optimization ◽  
Field Approximation ◽  
Mean Field Approximation ◽  
Energy Gradient ◽  
The Mean

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>

Molecular Dynamics Using Non-Variational Polarizable Force Fields: Theory, Periodic Boundary Conditions Implementation and Application to the Bond Capacity Model

2019 ◽  
Author(s):  
Pier Paolo Poier ◽  
Louis Lagardere ◽  
Jean-Philip Piquemal ◽  
Frank Jensen
Keyword(s):  
Boundary Conditions ◽  
Periodic Boundary ◽  
Force Fields ◽  
Periodic Boundary Conditions ◽  
Computational Effort ◽  
Polarizable Force Fields ◽  
Energy Models ◽  
Capacity Model ◽  
Energy Gradient ◽  
Polarization Models

<div> <div> <div> <p>We extend the framework for polarizable force fields to include the case where the electrostatic multipoles are not determined by a variational minimization of the electrostatic energy. Such models formally require that the polarization response is calculated for all possible geometrical perturbations in order to obtain the energy gradient required for performing molecular dynamics simulations. </p><div> <div> <div> <p>By making use of a Lagrange formalism, however, this computational demanding task can be re- placed by solving a single equation similar to that for determining the electrostatic variables themselves. Using the recently proposed bond capacity model that describes molecular polarization at the charge-only level, we show that the energy gradient for non-variational energy models with periodic boundary conditions can be calculated with a computational effort similar to that for variational polarization models. The possibility of separating the equation for calculating the electrostatic variables from the energy expression depending on these variables without a large computational penalty provides flexibility in the design of new force fields. </p><div><div><div> </div> </div> </div> <p> </p><div> <div> <div> <p>variables themselves. Using the recently proposed bond capacity model that describes molecular polarization at the charge-only level, we show that the energy gradient for non-variational energy models with periodic boundary conditions can be calculated with a computational effort similar to that for variational polarization models. The possibility of separating the equation for calculating the electrostatic variables from the energy expression depending on these variables without a large computational penalty provides flexibility in the design of new force fields. </p> </div> </div> </div> </div> </div> </div> </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.

Sign in / Sign up

Export Citation Format

Share Document