Random Forest Regressor based superconductivity materials investigation for critical temperature prediction

Author:

Revathy G.,Rajendran V.,Rashmika B.,Sathish Kumar P.,Parkavi P.,Shynisha J.

Publisher

Elsevier BV

Subject

General Medicine

Reference26 articles.

1. Prediction study on critical temperature (C) of different atomic numbers superconductors (both gaseous/solid elements) using machine learning techniques;Revathy;Mater. Today: Proc.,2021

2. A Data-Driven Statistical Model for Predicting the Critical Temperature of a Superconductor;Hamidaih;Comput. Mater. Sci.,2018

3. Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning, Special Issue;Li;Mater. Sci.: Synthesis, Structure, Properties,2020

4. T. D. Le, R. Noumeir, H. L. Quach, J. H. Kim, J. H. Kim and H. M. Kim, “Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach,” in IEEE Transactions on Applied Superconductivity, vol. 30, no. 4, pp. 1-5, June 2020, Art no. 8600105, 10.1109/TASC.2020.2971456.

5. Predicting new superconductors and their critical temperatures using unsupervised machine learning;Roter;Physica C (Amsterdam, Neth.),2020

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