Agreement between experiment and hybrid DFT calculations for O?H bond dissociation enthalpies in manganese complexes

2005 ◽  
Vol 26 (7) ◽  
pp. 661-667 ◽  
Author(s):  
Marcus Lundberg ◽  
Per E. M. Siegbahn
Keyword(s):  
Dft Calculations ◽  
Manganese Complexes ◽  
Bond Dissociation

scholarly journals An Accurate and Efficient Method to Predict Y-NO Bond Homolysis Bond Dissociation Energies

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Zhi Li ◽  
Lin Li ◽  
Zi Yan Zhong ◽  
Yi Han ◽  
LiHong Hu ◽  
...  
Keyword(s):  
Dft Calculations ◽  
Density Functional ◽  
Molecular Descriptors ◽  
Basis Set ◽  
Bond Dissociation Energies ◽  
Data Set ◽  
Test Set ◽  
Bond Dissociation ◽  
Dissociation Energies ◽  
Bond Homolysis

The paper suggests a new method that combines the Kennard and Stone algorithm (Kenstone, KS), hierarchical clustering (HC), and ant colony optimization (ACO)-based extreme learning machine (ELM) (KS-HC/ACO-ELM) with the density functional theory (DFT) B3LYP/6-31G(d) method to improve the accuracy of DFT calculations for the Y-NO homolysis bond dissociation energies (BDE). In this method, Kenstone divides the whole data set into two parts, the training set and the test set; HC and ACO are used to perform the cluster analysis on molecular descriptors; correlation analysis is applied for selecting the most correlated molecular descriptors in the classes, and ELM is the nonlinear model for establishing the relationship between DFT calculations and homolysis BDE experimental values. The results show that the standard deviation of homolysis BDE in the molecular test set is reduced from 4.03 kcal mol−1calculated by the DFT B3LYP/6-31G(d) method to 0.30, 0.28, 0.29, and 0.32 kcal mol−1by the KS-ELM, KS-HC-ELM, and KS-ACO-ELM methods and the artificial neural network (ANN) combined with KS-HC, respectively. This method predicts accurate values with much higher efficiency when compared to the larger basis set DFT calculation and may also achieve similarly accurate calculation results for larger molecules.

scholarly journals Study on Chemical Bond Dissociation and the Removal of Oxygen-Containing Functional Groups of Low-Rank Coal during Hydrothermal Carbonization: DFT Calculations

ACS Omega ◽  
2021 ◽  
Vol 6 (39) ◽  
pp. 25772-25781
Author(s):  
Han Dang ◽  
Guangwei Wang ◽  
Chunmei Yu ◽  
Xiaojun Ning ◽  
Jianliang Zhang ◽  
...  
Keyword(s):  
Dft Calculations ◽  
Chemical Bond ◽  
Functional Groups ◽  
Hydrothermal Carbonization ◽  
Low Rank ◽  
Low Rank Coal ◽  
Bond Dissociation

scholarly journals Machine learning enabling prediction of the bond dissociation enthalpy of hypervalent iodine from SMILES

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Masaya Nakajima ◽  
Tetsuhiro Nemoto
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Density Functional ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Hypervalent Iodine ◽  
Bond Dissociation Enthalpy ◽  
Important Indicator ◽  
Bond Dissociation ◽  
Dissociation Enthalpy

AbstractMachine learning to create models on the basis of big data enables predictions from new input data. Many tasks formerly performed by humans can now be achieved by machine learning algorithms in various fields, including scientific areas. Hypervalent iodine compounds (HVIs) have long been applied as useful reactive molecules. The bond dissociation enthalpy (BDE) value is an important indicator of reactivity and stability. Experimentally measuring the BDE value of HVIs is difficult, however, and the value has been estimated by quantum calculations, especially density functional theory (DFT) calculations. Although DFT calculations can access the BDE value with high accuracy, the process is highly time-consuming. Thus, we aimed to reduce the time for predicting the BDE by applying machine learning. We calculated the BDE of more than 1000 HVIs using DFT calculations, and performed machine learning. Converting SMILES strings to Avalon fingerprints and learning using a traditional Elastic Net made it possible to predict the BDE value with high accuracy. Furthermore, an applicability domain search revealed that the learning model could accurately predict the BDE even for uncovered inputs that were not completely included in the training data.

scholarly journals Catalytic atroposelective synthesis of axially chiral benzonitriles via chirality control during bond dissociation and CN group formation

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Ya Lv ◽  
Guoyong Luo ◽  
Qian Liu ◽  
Zhichao Jin ◽  
Xinglong Zhang ◽  
...  
Keyword(s):  
Dft Calculations ◽  
Kinetic Resolution ◽  
Group Formation ◽  
Heterocyclic Carbenes ◽  
Large Set ◽  
Dynamic Kinetic Resolution ◽  
Catalytic Activities ◽  
Bond Dissociation ◽  
Axially Chiral ◽  
Chirality Control

AbstractThe applications of axially chiral benzonitriles and their derivatives remain mostly unexplored due to their synthetic difficulties. Here we disclose an unusual strategy for atroposelective access to benzonitriles via formation of the nitrile unit on biaryl scaffolds pre-installed with stereogenic axes in racemic forms. Our method starts with racemic 2-arylbenzaldehydes and sulfonamides as the substrates and N-heterocyclic carbenes as the organocatalysts to afford axially chiral benzonitriles in good to excellent yields and enantioselectivities. DFT calculations suggest that the loss of p-toluenesulfinate group is both the rate-determining and stereo-determining step. The axial chirality is controlled during the bond dissociation and CN group formation. The reaction features a dynamic kinetic resolution process modulated by both covalent and non-covalent catalytic interactions. The axially chiral benzonitriles from our method can be easily converted to a large set of functional molecules that show promising catalytic activities for chemical syntheses and anti-bacterial activities for plant protections.

Mechanistic study of methanol oxidation by RuIV–oxo complexes

2015 ◽  
Vol 19 (01-03) ◽  
pp. 417-426
Author(s):  
Yoshihito Shiota ◽  
Shoya Takahashi ◽  
Shingo Ohzu ◽  
Tomoya Ishizuka ◽  
Takahiko Kojima ◽  
...  
Keyword(s):  
Dft Calculations ◽  
Methanol Oxidation ◽  
Density Functional ◽  
Activation Barrier ◽  
Hydrogen Abstraction ◽  
Hydroxyl Group ◽  
Mechanistic Study ◽  
Activation Barriers ◽  
Bond Dissociation ◽  
Oxo Complexes

The catalytic conversion of methanol to formaldehyde by three kinds of non-porphyrin Ru complexes, Ru IV O ( TPA ) (TPA = tris(2-pyridylmethyl)amine) (1a), Ru IV O (6- COO - TPA ) (6-COO-TPA = 2-(6-carboxyl-pyridyl)methyl-bis(2-pyridylmethyl)amine) (1b), and Ru IV O ( N4Py ) (N4Py = N,N-bis(2-pyridyl-methyl)-N-bis(2-pyridyl)methylamine) (1c), is discussed by using density functional theory (DFT) calculations. There are two possible reaction pathways for the oxidation of methanol to formaldehyde with respect to the first hydrogen abstraction from the methyl group (path 1) and the hydroxyl group (path 2). Path 1 and path 2 involve the hydroxymethyl radical (• CH 2 OH ) and the methoxyl radical ( CH 3 O •), respectively, as an intermediate. DFT calculations demonstrate that the two pathways are energetically comparable in the reactions by the three Ru IV –oxo complexes. The reactions with 1a and 1c are initiated by the C – H bond dissociation with activation barriers of 22.2 and 21.4 kcal/mol, respectively, while the reaction with 1b is initiated by the O – H bond dissociation with an activation barrier of 18.1 kcal/mol. However, the calculations showed that the rate-determining step is the H -atom abstraction from the CH 3 group of methanol in all the pathways. These results are in good agreement with kinetic analysis of the reactions by the Ru IV –oxo complexes, being useful for considering the mechanism of methanol oxidation.

Photoinduced Bond Dissociation of 4-Methylcoumarin Derivatives in Solution Studied by Laser Flash Photolysis and DFT Calculations

2009 ◽  
Vol 113 (20) ◽  
pp. 5815-5822 ◽  
Author(s):  
Minoru Yamaji ◽  
Koichi Nozaki ◽  
Xavier Allonas ◽  
Satoru Nakajima ◽  
Shozo Tero-Kubota ◽  
...  
Keyword(s):  
Dft Calculations ◽  
Flash Photolysis ◽  
Laser Flash Photolysis ◽  
Laser Flash ◽  
Bond Dissociation

Photoinduced ω-bond dissociation of m-halomethylbenzophenones studied by laser photolysis techniques and DFT calculations. Substituted position effects

2007 ◽  
Vol 9 (25) ◽  
pp. 3268-3275 ◽  
Author(s):  
Minoru Yamaji ◽  
Michiyo Ogasawara ◽  
Kazuhiro Kikuchi ◽  
Satoru Nakajima ◽  
Shozo Tero-Kubota ◽  
...  
Keyword(s):  
Dft Calculations ◽  
Laser Photolysis ◽  
Position Effects ◽  
Bond Dissociation
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