Comparison of Tree Based Ensemble Machine Learning Methods for Prediction of Rate Constant of Diels-Alder Reaction

2017 ◽  
pp. 997-1002 ◽  
Author(s):  
Vikrant A. Dev ◽  
Shounak Datta ◽  
Nishanth G. Chemmangattuvalappil ◽  
Mario R. Eden
Keyword(s):  
Machine Learning ◽  
Rate Constant ◽  
Alder Reaction ◽  
Diels Alder ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Diels Alder Reaction ◽  
Ensemble Machine Learning

scholarly journals Prediction of Concrete Properties Using Ensemble Machine Learning Methods

2020 ◽  
Vol 1625 ◽  
pp. 012024
Author(s):  
D Prayogo ◽  
D I Santoso ◽  
D Wijaya ◽  
T Gunawan ◽  
J A Widjaja
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Concrete Properties ◽  
Ensemble Machine Learning

Effect of high pressure on the rate constant of the Diels-Alder reaction between furan and maleic anhydride

1990 ◽  
Vol 39 (3) ◽  
pp. 456-459 ◽  
Author(s):  
V. M. Zhulin ◽  
M. V. Kel'tseva ◽  
V. S. Bogdanov ◽  
Yu. D. Koreshkov ◽  
E. B. Kabotyanskaya
Keyword(s):  
High Pressure ◽  
Maleic Anhydride ◽  
Rate Constant ◽  
Alder Reaction ◽  
Diels Alder ◽  
Diels Alder Reaction

scholarly journals Investigating Algorithmic Stock Market Trading using Ensemble Machine Learning Methods

Informatica ◽  
2020 ◽  
Vol 44 (3) ◽  
Author(s):  
Ramzi Saifan ◽  
Khaled Sharif ◽  
Mohammad Abu-Ghazaleh ◽  
Mohammad Abdel-Majeed
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning ◽  
Stock Market Trading

scholarly journals Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods

Buildings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 46
Author(s):  
Obuks Augustine Ejohwomu ◽  
Olakekan Shamsideen Oshodi ◽  
Majeed Oladokun ◽  
Oyegoke Teslim Bukoye ◽  
Nwabueze Emekwuru ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollutants ◽  
Data Science ◽  
Climatic Factors ◽  
Extreme Weather Events ◽  
Learning Methods ◽  
Rapid Urbanization ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning ◽  
Pm2.5 Concentration

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.

scholarly journals Machine Learning to Predict Diels-Alder Reaction Barriers from the Reactant State Electron Density

2021 ◽  
Author(s):  
Santiago Vargas ◽  
Matthew Hennefarth ◽  
Zhihao Liu ◽  
Anastassia N. Alexandrova
Keyword(s):  
Machine Learning ◽  
Chemical Reactivity ◽  
Alder Reaction ◽  
Diels Alder ◽  
Topological Descriptors ◽  
Artificial Enzyme ◽  
Diels Alder Reaction ◽  
Reaction Barriers ◽  
High Degree ◽  
Mechanical Charge

<div> <div> <div> <p>Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry, that countless variants, with or without catalysts, have been studied and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous, and can be used effectively for prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels-Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels-Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels-Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes in <i>k<sub>cat</sub></i>. We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments. </p> </div> </div> </div>

scholarly journals Machine Learning to Predict Diels-Alder Reaction Barriers from the Reactant State Electron Density

2021 ◽  
Author(s):  
Santiago Vargas ◽  
Matthew Hennefarth ◽  
Zhihao Liu ◽  
Anastassia N. Alexandrova
Keyword(s):  
Machine Learning ◽  
Chemical Reactivity ◽  
Alder Reaction ◽  
Diels Alder ◽  
Topological Descriptors ◽  
Artificial Enzyme ◽  
Diels Alder Reaction ◽  
Reaction Barriers ◽  
High Degree ◽  
Mechanical Charge

<div> <div> <div> <p>Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry, that countless variants, with or without catalysts, have been studied and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous, and can be used effectively for prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels-Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels-Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels-Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes in <i>k<sub>cat</sub></i>. We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments. </p> </div> </div> </div>

scholarly journals APPLICATION OF ENSEMBLE MACHINE LEARNING METHODS FOR PREDICTING THE TECHNICAL STATE OF AN OBJECT

2021 ◽  
Vol 23 (1) ◽  
pp. 111-114
Author(s):  
Yu.E. Kuvayskova ◽  
Keyword(s):  
Machine Learning ◽  
The State ◽  
Technical State ◽  
Supervised Machine Learning ◽  
Time Interval ◽  
Technical Documentation ◽  
Technical Object ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

To ensure the reliable functioning of a technical object, it is necessary to predict its state for the upcoming time interval. Let the technical state of the object be characterized at a certain point in time by a set of parameters established by the technical documentation for the object. It is assumed that for certain values of these parameters, the object may be in a good or faulty state. It is required by the values of these parameters to estimate the state of the object in the upcoming time interval. Supervised machine learning methods can be applied to solve this problem. However, to obtain good results in predicting the state of an object, it is necessary to choose the correct training model. One of the disadvantages of machine learning models is high bias and too much scatter. In this paper, to reduce the scatter of the model, it is proposed to use ensemble machine learning methods, namely, the bagging procedure. The main idea of the ensemble of methods is that with the right combination of weak models, more accurate and robust models can be obtained. The purpose of bagging is to create an ensemble model that is more reliable than the individual models that make up it. One of the big advantages of bagging is its concurrency, since different ensemble models are trained independently of each other. The effectiveness of the proposed approach is shown by the example of predicting the technical state of an object by eight parameters of its functioning. To assess the effectiveness of the application of ensemble machine learning methods for predicting the technical state of an object, the quality criteria of binary classification are used: accuracy, completeness, and F-measure. It is shown that the use of ensemble machine learning methods can improve the accuracy of predicting the state of a technical object by 4% –9% in comparison with basic machine learning methods. This approach can be used by specialists to predict the technical condition of objects in many technical applications, in particular, in aviation.

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