scholarly journals Machine learning for the structure–energy–property landscapes of molecular crystals

2018 ◽  
Vol 9 (5) ◽  
pp. 1289-1300 ◽  
Author(s):  
Félix Musil ◽  
Sandip De ◽  
Jack Yang ◽  
Joshua E. Campbell ◽  
Graeme M. Day ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Crystals ◽  
Chemical Properties ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physical Chemical ◽  
Learning Techniques ◽  
Structure Calculations ◽  
Energy Property

Polymorphism is common in molecular crystals, whose energy landscapes usually contain many structures with similar stability, but very different physical–chemical properties. Machine-learning techniques can accelerate the evaluation of energy and properties by side-stepping accurate but demanding electronic-structure calculations, and provide a data-driven classification of the most important molecular packing motifs.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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