scholarly journals Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models

2017 ◽  
Vol 121 (10) ◽  
pp. 5612-5619 ◽  
Author(s):  
Raymond Gasper ◽  
Hongbo Shi ◽  
Ashwin Ramasubramaniam
Keyword(s):  
Machine Learning ◽  
Decomposition Analysis ◽  
Pt Nanoparticles ◽  
Low Energy ◽  
Energy Decomposition Analysis ◽  
Learning Models ◽  
Energy Decomposition ◽  
Adsorption Of Co ◽  
Low Symmetry ◽  
Machine Learning Models

scholarly journals Effect of Unwanted Guest Molecules on the Stacking Configuration of Covalent Organic Frameworks: A Periodic Energy Decomposition Analysis

2020 ◽  
Author(s):  
A.D. Dinga Wonanke ◽  
Mathew Addicoat
Keyword(s):  
Guest Molecule ◽  
Dispersion Model ◽  
Critical Role ◽  
Decomposition Analysis ◽  
Empirical Method ◽  
Low Energy ◽  
Energy Decomposition Analysis ◽  
Energy Decomposition ◽  
Guest Molecules ◽  
Semi Empirical

Elucidating the precise stacking configuration of a covalent organic framework, COF, is critical to fully understand their various applications. Unfortunately, most COFs form powder crystals whose atomic characterisations are possible only through powder X-ray diffraction (PXRD) analysis. However, this analysis has to be coupled with computational simulations, wherein computed PXRD patterns for different stacking configurations are compared with experimental patterns to predict the precise stacking configuration. This task is often computationally challenging firstly because, computation of these systems mostly rely on the use of semi-empirical methods that need to be adequately parametrised for the system being studied and secondly because some of these compounds possess guest molecules, which are not often taken into account during computation. COF-1 is an extreme case in which the presence of the guest molecule plays a critical role in predicting the precise stacking configuration. Using this as a case study, we mapped out a full PES for the stacking configuration in the guest free and guest containing system using the GFN-xTB semi-empirical method followed by a periodic energy decomposition analysis using first principle DFT. Our results showed that the presence of the guest molecule leads to multiple low energy stacking configurations with significantly different lateral offsets. Also, the semi-empirical method does not precisely predict DFT low energy configurations, however, it accurately accounts for dispersion. Finally, our quantum-mechanical analysis demonstrates that electrostatic-dispersion model suggested Hunter and Sanders accurately describe the stacking in 2D COFs as oppose to the newly suggested Pauli-dispersion model.

scholarly journals Effect of Unwanted Guest Molecules on the Stacking Configuration of Covalent Organic Frameworks: A Periodic Energy Decomposition Analysis

2020 ◽  
Author(s):  
A.D. Dinga Wonanke ◽  
Mathew Addicoat
Keyword(s):  
Guest Molecule ◽  
Dispersion Model ◽  
Critical Role ◽  
Decomposition Analysis ◽  
Empirical Method ◽  
Low Energy ◽  
Energy Decomposition Analysis ◽  
Energy Decomposition ◽  
Guest Molecules ◽  
Semi Empirical

Elucidating the precise stacking configuration of a covalent organic framework, COF, is critical to fully understand their various applications. Unfortunately, most COFs form powder crystals whose atomic characterisations are possible only through powder X-ray diffraction (PXRD) analysis. However, this analysis has to be coupled with computational simulations, wherein computed PXRD patterns for different stacking configurations are compared with experimental patterns to predict the precise stacking configuration. This task is often computationally challenging firstly because, computation of these systems mostly rely on the use of semi-empirical methods that need to be adequately parametrised for the system being studied and secondly because some of these compounds possess guest molecules, which are not often taken into account during computation. COF-1 is an extreme case in which the presence of the guest molecule plays a critical role in predicting the precise stacking configuration. Using this as a case study, we mapped out a full PES for the stacking configuration in the guest free and guest containing system using the GFN-xTB semi-empirical method followed by a periodic energy decomposition analysis using first principle DFT. Our results showed that the presence of the guest molecule leads to multiple low energy stacking configurations with significantly different lateral offsets. Also, the semi-empirical method does not precisely predict DFT low energy configurations, however, it accurately accounts for dispersion. Finally, our quantum-mechanical analysis demonstrates that electrostatic-dispersion model suggested Hunter and Sanders accurately describe the stacking in 2D COFs as oppose to the newly suggested Pauli-dispersion model.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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