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

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.

Time-resolved EPR and laser photolysis investigations of photoinduced ω-bond dissociation in an aromatic carbonyl compound having triplet π,π* character

2006 ◽  
Vol 417 (1-3) ◽  
pp. 211-216 ◽  
Author(s):  
Minoru Yamaji ◽  
Susumu Inomata ◽  
Satoru Nakajima ◽  
Kimio Akiyama ◽  
Shozo Tero-Kubota ◽  
...  
Keyword(s):  
Carbonyl Compound ◽  
Laser Photolysis ◽  
Time Resolved ◽  
Bond Dissociation

Diradicalized biphenyl derivative carbon-based material molecules: exploring the tuning effects on magnetic couplings

2017 ◽  
Vol 19 (8) ◽  
pp. 5932-5943 ◽  
Author(s):  
Yiwei Feng ◽  
Fengying Zhang ◽  
Xinyu Song ◽  
Yuxiang Bu
Keyword(s):  
Magnetic Properties ◽  
Dft Calculations ◽  
Interference Effects ◽  
Position Effects ◽  
Biphenyl Derivative ◽  
Magnetic Couplings ◽  
Carbon Based

DFT calculations reveal three different interference effects on the magnetic properties of carbon-based molecule coupled nitroxide diradicals: twisting, sideways group, and position effects.

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

Laser photolysis studies of ω-bond dissociation in aromatic carbonyls with a C–C triple bond stimulated by triplet sensitization

2017 ◽  
Vol 19 (26) ◽  
pp. 17028-17035 ◽  
Author(s):  
Minoru Yamaji ◽  
Ami Horimoto ◽  
Bronislaw Marciniak
Keyword(s):  
Carbonyl Compounds ◽  
Phenyl Ring ◽  
Triple Bond ◽  
Laser Photolysis ◽  
Phenyl Sulfide ◽  
Bond Dissociation

We have prepared three types of carbonyl compounds, benzoylethynylmethyl phenyl sulfide (2@SPh), (p-benzoyl)phenylethynylmethyl phenyl sulfide (3@SPh) andp-(benzoylethynyl)benzyl phenyl sulfide (4@SPh) with benzoyl and phenylthiylmethyl groups, which are interconnected with a C–C triple bond and a phenyl ring.

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.

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