Predict Unknown Properties of Elements of Periodic Table with Machine Learning

Abstract

Yash K. Bhatia, Maxwell Gao, Sruthi Ainapurapu, Abhilash Akula, Elizabeth A. Christophy, Payam Norouzzadeh, Steven Buckner and Bahareh Rahmani

The periodic table of elements includes 92 elements with many unknown properties like melting point, boiling point, heat of vaporization, and molar heat capacity of some specific elements such as Curium, Berkelium, Californium, and Einsteinium. Physicists, chemists, and other scientists have done many successful experiments to predict these mysterious features using the first principal methods. But still many properties have been unclear.

In this project we apply Machine Learning models such as linear and logistic regression to predict this unknow values. The known values split to train and test data to find and confirm the model. Then the model will be run over unknown variables.

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