Machine Learning Approach for Predicting Nucleophilicity of Organic Molecules

2021 ◽  
Author(s):  
Vaneet Saini ◽  
Aditya Sharma ◽  
Dhruv Nivatia
Keyword(s):  
Machine Learning ◽  
Experimental Determination ◽  
Organic Molecules ◽  
Chemical Reactivity ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Intensive Approach

Nucleophilicity provides an important information about chemical reactivity of organic molecules. Experimental determination of nucleophilicity parameter is tedious and resource-intensive approach. Herein, we present a novel machine learning protocol that...

scholarly journals Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach

RSC Advances ◽  
2020 ◽  
Vol 10 (40) ◽  
pp. 23834-23841
Author(s):  
Zong-Rong Ye ◽  
I.-Shou Huang ◽  
Yu-Te Chan ◽  
Zhong-Ji Li ◽  
Chen-Cheng Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Molecules ◽  
Fluorescence Emission ◽  
Emission Wavelength ◽  
Learning Approach ◽  
Computationally Efficient ◽  
Machine Learning Approach ◽  
Efficient Prediction

The combinatorial QSAR and machine learning approach provides the qualitative and computationally efficient prediction for fluorescence emission wavelength of organic molecules.

scholarly journals Predicting the chemical reactivity of organic materials using a machine-learning approach

2020 ◽  
Vol 11 (30) ◽  
pp. 7813-7822 ◽  
Author(s):  
Byungju Lee ◽  
Jaekyun Yoo ◽  
Kisuk Kang
Keyword(s):  
Machine Learning ◽  
Organic Materials ◽  
Chemical Reactivity ◽  
Functional Materials ◽  
Chemical Components ◽  
Learning Approach ◽  
System P ◽  
Machine Learning Approach ◽  
Selection Of

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system.

scholarly journals Temporal and Spatial Detection of the Onset of Local Necking and Assessment of its Growth Behavior

Materials ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2427
Author(s):  
Christian Jaremenko ◽  
Emanuela Affronti ◽  
Marion Merklein ◽  
Andreas Maier
Keyword(s):  
Machine Learning ◽  
Growth Behavior ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Material Failure ◽  
Local Necking ◽  
Machine Learning Approach ◽  
Strain Paths ◽  
Temporal And Spatial

This study proposes a method for the temporal and spatial determination of the onset of local necking determined by means of a Nakajima test set-up for a DC04 deep drawing and a DP800 dual-phase steel, as well as an AA6014 aluminum alloy. Furthermore, the focus lies on the observation of the progress of the necking area and its transformation throughout the remainder of the forming process. The strain behavior is learned by a machine learning approach on the basis of the images when the process is close to material failure. These learned failure characteristics are transferred to new forming sequences, so that critical areas indicating material failure can be identified at an early stage, and consequently enable the determination of the beginning of necking and the analysis of the necking area. This improves understanding of the necking behavior and facilitates the determination of the evaluation area for strain paths. The growth behavior and traceability of the necking area is objectified by the proposed weakly supervised machine learning approach, thereby rendering a heuristic-based determination unnecessary. Furthermore, a simultaneous evaluation on image and pixel scale is provided that enables a distinct selection of the failure quantile of the probabilistic forming limit curve.

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