Aerobic Oxidation of Methanol to Formic Acid on Au8–: Benchmark Analysis Based on Completely Renormalized Coupled-Cluster and Density Functional Theory Calculations

2013 ◽  
Vol 117 (40) ◽  
pp. 10416-10427 ◽  
Author(s):  
Jared A. Hansen ◽  
Masahiro Ehara ◽  
Piotr Piecuch
Keyword(s):  
Density Functional Theory ◽  
Formic Acid ◽  
Density Functional ◽  
Aerobic Oxidation ◽  
Density Functional Theory Calculations ◽  
Coupled Cluster ◽  
Functional Theory ◽  
Benchmark Analysis ◽  
Oxidation Of Methanol

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>

Binuclear O2 activation and hydrogen transfer mechanism for aerobic oxidation of alcohols

2020 ◽  
Vol 10 (7) ◽  
pp. 2183-2192
Author(s):  
Zhiyun Hu ◽  
Hongyu Ge ◽  
Xinzheng Yang
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Hydrogen Transfer ◽  
Aerobic Oxidation ◽  
Density Functional Theory Calculations ◽  
Transfer Mechanism ◽  
Functional Theory ◽  
O2 Activation ◽  
Oxidation Of Alcohols

Density functional theory calculations reveal a binuclear O2 activation and hydrogen transfer mechanism with spin-crossovers for aerobic oxidation of alcohols.

scholarly journals Theoretical study of the oxidation of formic acid on a PtPd(111) surface

2019 ◽  
Vol 44 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Ying-Ying Wang
Keyword(s):  
Density Functional Theory ◽  
Formic Acid ◽  
Density Functional ◽  
Main Product ◽  
Theoretical Study ◽  
Reaction Pathway ◽  
Density Functional Theory Calculations ◽  
Acid Decomposition ◽  
Functional Theory ◽  
Formic Acid Decomposition

By performing density functional theory calculations, the adsorption configurations of formic acid and possible reaction pathway for HCOOH oxidation on PtPd(111) surface are located. Results show that CO2 is preferentially formed as the main product of the catalytic oxidation of formic acid. The formation of CO on the pure Pd surface could not possibly occur during formic acid decomposition on the PtPd(111) surface owing to the high reaction barrier. Therefore, no poisoning of catalyst would occur on the PtPd(111) surface. Our results indicate that the significantly increased catalytic activity of bimetallic PtPd catalyst towards HCOOH oxidation should be attributed to the reduction in poisoning by CO.

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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