State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning

Description

The dataset for the  review paper titled "State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning" consists of the four files with the names (i) alloy_names.csv, (ii) machine_learning_methods.csv, (iii) nomenclature.csv, and (iv) ptmc_terminologies.csv.

(i) alloy_names.csv : This file presents the summarized list of alloys' names which have been discussed in the review paper. The list thus provides the names of the alloys for which data-driven studies have been attempted to explore one of the effects - martensitic transformation, phase transformation or shape memory effect. 

(ii) machine_learning_methods.csv : The machine learning methods that have been discussed in the review paper in relation to the simulation, modeling or prediction tasks in martensitic alloys are listed in this file. This csv file conssits of three columns. The first column "Methods" lists the names of the machine learning methods whereas the second column "Purpose" briefly reveals the objective of the use of the named machine learning method. The final column "Reference" provides the information about the original work (source) from which the data is obtained. 

(iii) nomenclature.csv : This file lists all of the acronyms utilized in the review paper, and provides their corresponding full forms. 

 (iv) ptmc_terminologies.csv : One of the major theories considered significant in the study of martensitic alloys and shape memory effects is phenomenological theory of martensite crystallography (PTMC). The review paper discusses this theory. The different concepts that might be helpful in understanding PTMC , have been assembled in the form of terminologies.