Density Functional Theory, Calculations of Potential Energy Surfaces and Reaction Paths

1995 ◽  
pp. 161-189 ◽  
Author(s):  
Gotthard Seifert ◽  
Kerstin Krüger
Keyword(s):  
Density Functional Theory ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Reaction Paths ◽  
Functional Theory ◽  
Energy Surfaces

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>

Theoretical Investigation of the Decarbonylation of Acetaldehyde by Ni+2 Using Density Functional Theory

2013 ◽  
Vol 446-447 ◽  
pp. 168-171
Author(s):  
Hong Fei Liu ◽  
Xin Min Min ◽  
Hai Xia Yang
Keyword(s):  
Density Functional Theory ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Density Functional ◽  
Transition Metal Ions ◽  
Basis Sets ◽  
Functional Theory ◽  
Representative System ◽  
Energy Surfaces ◽  
Ground Potential

The decarbonylation of acetaldehyde assisted by Ni+2, which was selected as a representative system of transition metal ions assisted decarbonylation of acetaldehyde, has been investigated using density functional theory (B3LYP) in conjunction with the 6-31+G** basis sets in C,H,O atoms and Lanl2dz basis sets in Ni atom The geometries and energies of the reactants, intermediates, products and transition states relevant to the reaction were located on the triplet ground potential energy surfaces of [Ni, O, C2,H4]+2. Our calculations indicate the decarbonylation of acetaldehyde takes place through four steps, that is, encounter complexation, CC activation, aldehyde H-shift and nonreactive dissociation, it is that CC activation by Ni+2that lead to the decarbonylation of acetaldehyde.

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