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.

Solvent Effect on Bond Dissociation Enthalpy (BDE) of Tetrahydrocurcumin: A Theoretical Study

2019 ◽  
Vol 966 ◽  
pp. 215-221
Author(s):  
Lusia Silfia Pulo Boli ◽  
Nufida Dwi Aisyah ◽  
Vera Khoirunisa ◽  
Heni Rachmawati ◽  
Hermawan Kresno Dipojono ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Solvent Effect ◽  
Functional Groups ◽  
Density Functional ◽  
Hydrogen Transfer ◽  
Bond Dissociation Enthalpy ◽  
Polar Medium ◽  
Bond Dissociation ◽  
Dissociation Enthalpy ◽  
Frequency Calculation

Solvent effect on bond dissociation enthalpy (BDE) of different functional groups of tetrahydrocurcumin is investigated. This is to evaluate how the polarity of a medium affect BDE and to clarify which functional groups hold the key role in its antioxidant activity through hydrogen transfer. We occupy density functional theory to calculate BDE through geometrical optimization and frequency calculation at six sites of tetrahydrocurcumin in water, methanol and chloroform solvents. The solvents represent polar and non-polar medium. Our result shows that BDE is lower in non-polar medium and hydrogen transfer is favored in this medium. A phenolic group is responsible for the antioxidant activity of tetrahydrocurcumin.

Sublimation study of triphenyl boron and the bond-dissociation enthalpy of BC6H5

1984 ◽  
Vol 16 (8) ◽  
pp. 703-709 ◽  
Author(s):  
Steven W. Govorchin ◽  
Adli S. Kana'an ◽  
Joseph M. Kanamueller
Keyword(s):  
Bond Dissociation Enthalpy ◽  
Bond Dissociation ◽  
Dissociation Enthalpy

scholarly journals Mechanistic investigation into phenol oxidation by IBX elucidated by DFT calculations

2020 ◽  
Vol 18 (6) ◽  
pp. 1117-1129 ◽  
Author(s):  
Amritpal Kaur ◽  
Alireza Ariafard
Keyword(s):  
Density Functional Theory ◽  
Dft Calculations ◽  
Density Functional ◽  
Phenol Oxidation ◽  
Hypervalent Iodine ◽  
Functional Theory ◽  
Mechanistic Investigation ◽  
Oxidation Of Phenols ◽  
Double Oxidation

Density functional theory (DFT) at the SMD/M06-2X/def2-TZVP//SMD/M06-2X/LANL2DZ(d),6-31G(d) level was used to explore the regioselective double oxidation of phenols by a hypervalent iodine(v) reagent (IBX) to give o-quinones.

scholarly journals Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2182
Author(s):  
Damilola Ologunagba ◽  
Shyam Kattel
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Bimetallic Catalysts ◽  
Density Functional ◽  
Surface Segregation ◽  
Surface Composition ◽  
Bulk Composition ◽  
Statistical Machine Learning ◽  
Bimetallic Surfaces ◽  
Computing Platforms

Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties.

scholarly journals Data-driven studies of magnetic two-dimensional materials

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Trevor David Rhone ◽  
Wei Chen ◽  
Shaan Desai ◽  
Steven B. Torrisi ◽  
Daniel T. Larson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Density Functional ◽  
Magnetic Coupling ◽  
Magnetic Ordering ◽  
Magnetic Behavior ◽  
Data Driven ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Data Driven Approach

Abstract We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form $$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$ A 2 B 2 X 6 , based on the known material $$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$ Cr 2 Ge 2 Te 6 , using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

scholarly journals Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Gabriel Cifuentes-Alcobendas ◽  
Manuel Domínguez-Rodrigo
Keyword(s):  
Machine Learning ◽  
Accurate Method ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Accurate Identification ◽  
Cut Marks ◽  
Eating Meat ◽  
Microscopic Features ◽  
Immense Potential

AbstractAccurate identification of bone surface modifications (BSM) is crucial for the taphonomic understanding of archaeological and paleontological sites. Critical interpretations of when humans started eating meat and animal fat or when they started using stone tools, or when they occupied new continents or interacted with predatory guilds impinge on accurate identifications of BSM. Until now, interpretations of Plio-Pleistocene BSM have been contentious because of the high uncertainty in discriminating among taphonomic agents. Recently, the use of machine learning algorithms has yielded high accuracy in the identification of BSM. A branch of machine learning methods based on imaging, computer vision (CV), has opened the door to a more objective and accurate method of BSM identification. The present work has selected two extremely similar types of BSM (cut marks made on fleshed an defleshed bones) to test the immense potential of artificial intelligence methods. This CV approach not only produced the highest accuracy in the classification of these types of BSM until present (95% on complete images of BSM and 88.89% of images of only internal mark features), but it also has enabled a method for determining which inconspicuous microscopic features determine successful BSM discrimination. The potential of this method in other areas of taphonomy and paleobiology is enormous.

scholarly journals A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery

2019 ◽  
Vol 8 (5) ◽  
pp. 668 ◽  
Author(s):  
Yang Cao ◽  
Xin Fang ◽  
Johan Ottosson ◽  
Erik Näslund ◽  
Erik Stenberg
Keyword(s):  
Machine Learning ◽  
Bariatric Surgery ◽  
Test Data ◽  
Imbalanced Data ◽  
Preoperative Assessment ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Global Public Health ◽  
Bariatric Surgical Procedure ◽  
A Minor

Background: Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. So far, traditional statistical methods have failed to produce high accuracy. We aimed to find a useful machine learning (ML) algorithm to predict the risk for severe complication after bariatric surgery. Methods: We trained and compared 29 supervised ML algorithms using information from 37,811 patients that operated with a bariatric surgical procedure between 2010 and 2014 in Sweden. The algorithms were then tested on 6250 patients operated in 2015. We performed the synthetic minority oversampling technique tackling the issue that only 3% of patients experienced severe complications. Results: Most of the ML algorithms showed high accuracy (>90%) and specificity (>90%) in both the training and test data. However, none of the algorithms achieved an acceptable sensitivity in the test data. We also tried to tune the hyperparameters of the algorithms to maximize sensitivity, but did not yet identify one with a high enough sensitivity that can be used in clinical praxis in bariatric surgery. However, a minor, but perceptible, improvement in deep neural network (NN) ML was found. Conclusion: In predicting the severe postoperative complication among the bariatric surgery patients, ensemble algorithms outperform base algorithms. When compared to other ML algorithms, deep NN has the potential to improve the accuracy and it deserves further investigation. The oversampling technique should be considered in the context of imbalanced data where the number of the interested outcome is relatively small.

scholarly journals Iterative training set refinement enables reactive molecular dynamics via machine learned forces

RSC Advances ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 4293-4299 ◽  
Author(s):  
Lei Chen ◽  
Ivan Sukuba ◽  
Michael Probst ◽  
Alexander Kaiser
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Dft Calculations ◽  
High Accuracy ◽  
Iterative Refinement ◽  
Training Set ◽  
Reactive Molecular Dynamics

Reactive self-sputtering from a Be surface is simulated using neural network trained forces with high accuracy. The key in machine learning from DFT calculations is a well-balanced and complete training set of energies and forces obtained by iterative refinement.

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