Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor

TL;DR

This paper applies machine learning, specifically tree-based models, to predict the critical temperature of liquid metal alloy superconductors, demonstrating high accuracy and aiding in discovering new high-performance superconducting materials.

Contribution

It is the first to use machine learning to predict Tc of liquid metal alloy superconductors, extending existing models to a large set of alloys for accelerated discovery.

Findings

Extra Trees model achieved R2 of 0.9519 and RMSE of 6.2624 K.

Predicted highest Tc of 7.01 K for In0.5Sn0.5 alloy.

Extended predictions to over 48,000 alloys across 66 elements.

Abstract

Superconductors, which are crucial for modern advanced technologies due to their zero-resistance properties, are limited by low Tc and the difficulty of accurate prediction. This article made the initial endeavor to apply machine learning to predict the critical temperature (Tc) of liquid metal (LM) alloy superconductors. Leveraging the SuperCon dataset, which includes extensive superconductor property data, we developed a machine learning model to predict Tc. After addressing data issues through preprocessing, we compared multiple models and found that the Extra Trees model outperformed others with an R2 of 0.9519 and an RMSE of 6.2624 K. This model is subsequently used to predict Tc for LM alloys, revealing In0.5Sn0.5 as having the highest Tc at 7.01 K. Furthermore, we extended the prediction to 2,145 alloys binary and 45,670 ternary alloys across 66 metal elements and promising results were achieved. This work demonstrates the advantages of tree-based models in predicting Tc and would help accelerate the discovery of high-performance LM alloy superconductors in the coming time.