Coupled Cluster and Density Functional Theory Studies of the Vibrational Contribution to the Optical Rotation of (S)-Propylene Oxide

2006 ◽  
Vol 128 (3) ◽  
pp. 976-982 ◽  
Author(s):  
Jacob Kongsted ◽  
Thomas Bondo Pedersen ◽  
Lasse Jensen ◽  
Aage E. Hansen ◽  
Kurt V. Mikkelsen
Keyword(s):  
Density Functional Theory ◽  
Propylene Oxide ◽  
Density Functional ◽  
Optical Rotation ◽  
Coupled Cluster ◽  
Functional Theory ◽  
Vibrational Contribution

scholarly journals A computational study for the reaction mechanism of metal-free cyanomethylation of aryl alkynoates with acetonitrile

RSC Advances ◽  
2021 ◽  
Vol 11 (30) ◽  
pp. 18246-18251
Author(s):  
Selçuk Eşsiz
Keyword(s):  
Density Functional Theory ◽  
Reaction Mechanism ◽  
Density Functional ◽  
Computational Study ◽  
Coupled Cluster ◽  
Functional Theory ◽  
Metal Free ◽  
Coupled Cluster Methods ◽  
High Level ◽  
Cluster Methods

A computational study of metal-free cyanomethylation and cyclization of aryl alkynoates with acetonitrile is carried out employing density functional theory and high-level coupled-cluster methods, such as [CCSD(T)].

Estimation of optical rotation of γ-alkylidenebutenolide, cyclopropylamine, cyclopropyl-methanol and cyclopropenone based compounds by a Density Functional Theory (DFT) approach

Chirality ◽  
2017 ◽  
Vol 29 (10) ◽  
pp. 634-647
Author(s):  
Iram Shahzadi ◽  
Aqsa Shaukat ◽  
Zeenat Zara ◽  
Muhammad Irfan ◽  
Bertil Eliasson ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Optical Rotation ◽  
Functional Theory

scholarly journals The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

2020 ◽  
Author(s):  
Justin S. Smith ◽  
Roman Zubatyuk ◽  
Benjamin T. Nebgen ◽  
Nicholas Lubbers ◽  
Kipton Barros ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Potential Energy Surfaces ◽  
Density Functional ◽  
Atomic Charge ◽  
Density Functional Theory Calculations ◽  
Coupled Cluster ◽  
Data Sets ◽  
Data Set ◽  
Functional Theory ◽  
Energy Surfaces

<p>Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based eneral-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data set diversity, we visualize them with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5M density functional theory calculations, while the ANI-1ccx data set contains 500k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM properties from density functional theory and coupled cluster are provided: energies, atomic forces, multipole moments, atomic charges, and more. We provide this data to the community to aid research and development of ML models for chemistry.</p>

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