Thirty Years of Geometry Optimization in Quantum Chemistry and Beyond: A Tribute to Berny Schlegel

2012 ◽  
Vol 8 (12) ◽  
pp. 4853-4855
Author(s):  
Hrant P. Hratchian ◽  
Xiaosong Li
Keyword(s):  
Quantum Chemistry ◽  
Geometry Optimization

scholarly journals Troubleshooting unstable molecules in chemical space

2021 ◽  
Vol 12 (15) ◽  
pp. 5566-5573
Author(s):  
Salini Senthil ◽  
Sabyasachi Chakraborty ◽  
Raghunathan Ramakrishnan
Keyword(s):  
Big Data ◽  
Quantum Chemistry ◽  
High Throughput ◽  
Chemical Space ◽  
Geometry Optimization ◽  
Data Generation ◽  
Structural Rearrangements ◽  
Unstable Molecules

A high-throughput workflow for connectivity preserving geometry optimization minimizes unintended structural rearrangements during quantum chemistry big data generation.

Modeling the Effects of Solvation on the Structure and Properties of Optical Limiting Materials Using Ab Initio Quantum Chemistry

1997 ◽  
Vol 479 ◽  
Author(s):  
P. N. Day ◽  
Z. Wang ◽  
R. Pachter
Keyword(s):  
Quantum Chemistry ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
Single Point ◽  
Geometry Optimization ◽  
Structure And Properties ◽  
Solvation Model ◽  
Effective Fragment Potential ◽  
Solvent Models ◽  
Exchange Repulsion

Abstracthe Effective Fragment Potential (EFP) model of solvation can now be extended beyond aqueous systems due to the development of transferable exchange repulsion potentials. EFPs for methanol and chloroform have been developed, and calculations with these new EFPs agree well with full ab initio calculations. Ab initio calculations have been carried out on zinc tetraphenyl-octobromyl-porphyrin both with and without the EFP solvation model. While the aqueous calculation, which had its geometry optimized, gave good results, the single-point calculations carried out with the two new solvent models indicate the need for geometry optimization.

COMPLETE SPACE QUANTUM CHEMISTRY BY MULTIRESOLUTION MULTIWAVELET BASIS SET

2013 ◽  
Vol 11 (04) ◽  
pp. 1360008 ◽  
Author(s):  
HIDEO SEKINO ◽  
AKIRA MATSUMURA ◽  
YUKINA YOKOI ◽  
TETSUYA KATO
Keyword(s):  
Quantum Chemistry ◽  
Geometry Optimization ◽  
Vibrational Analysis ◽  
Molecular Geometry ◽  
Basis Set ◽  
Basis Sets ◽  
Chemical Information ◽  
Gaussian Basis Sets ◽  
Complete Space ◽  
Complete Basis Set

Quantum chemistry program based on Multiresolution Multiwavelet (MRMW) basis set is much simpler and can be more efficient than the conventional one based on Gaussian basis set in molecular geometry optimization because the Hellmann Fynman Theorem (HFT) holds for the complete space which MRMW provides. It is numerically shown that large Gaussian basis sets can provide the chemical information with chemical accuracy for very small molecular systems despite the incompleteness of their basis set. However, even for the relatively small size molecule of CH4 , the error from the basis set incompleteness results in an error larger than an acceptable chemical accuracy. The advantage of the MRMW complete basis set in vibrational analysis is also discussed.

Quantum Chemistry.

1958 ◽  
Vol 17 (3_4) ◽  
pp. 279-280
Author(s):  
Th. Förster
Keyword(s):  
Quantum Chemistry

Propagators in Quantum Chemistry

1975 ◽  
Vol 95 (4-6) ◽  
pp. 318-319
Author(s):  
W. A. Bingel
Keyword(s):  
Quantum Chemistry

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.

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