Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors - Gradient Boosted Feature Selection (GBFS)

Aug 4, 2026

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Accurate prediction of superconductors’ critical temperatures (Tc) remains difficult because superconducting behavior depends on complex chemical and structural factors. A machine-learning framework is presented to clarify structure-property and property-property relationships using only chemical composition–derived, high-dimensional features. The Gradient Boosted Feature Selection (GBFS) workflow integrates exploratory data analysis, statistical evaluation, and multicollinearity reduction to select relevant features. Results on ~16,400 compounds (≈12,000 compositions) support both classification for Tc > 10 K and regression for Tc prediction, with strong F1, AUC-ROC, precision, and regression error metrics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages27

Reading Time1 h 8 min

AccessFree to download

Abstract

Introduction

Superconductivity background and critical temperature (Tc)

Limitations of existing theories

Data-driven materials discovery and machine learning workflow

This article is licensed under CC-BY 4.0 [pubs.acs.org/jcim](pubs.acs.org/jcim) Article Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors Son Gyo Jung, Guwon Jung, and Jacqueline M. Cole * Cite This: J. Chem. Inf. Model. 2024, 64, 7349−7375 Read Online | | | | | | | | --- | --- | --- | --- | --- | --- | | ACCESS | Metrics & More | | Article Recommendations | | *sı Supporting Information | ABSTRACT: In the quest for advanced superconducting materials, the accurate prediction of critical temperatures (Tc) poses a formidable challenge, largely due to the complex interdependencies between superconducting properties and the chemical and structural characteristics of a given material. To address this challenges, we have developed a machine-learning framework that aims to elucidate these complicated and hitherto poorly understood structure−property and property−property relationships. This study introduces a novel machine-learningbased workflow, termed the Gradient Boosted Feature Selection (GBFS), which has been tailored to predict Tc for superconductors by employing a distributed gradient-boosting framework. This approach integrates exploratory data analyses, statistical evalua tions, and multicollinearity reduction techniques to select highly relevant features from a high-dimensional feature space, derived solely from the chemical composition of materials. Our methodology was rigorously tested on a data set comprising approximately 16,400 chemical compounds with around 12,000 unique chemical compositions. The GBFS workflow enabled the development of a classification model that distinguishes compositions likely to exhibit Tc values greater than 10 K. This model achieved a weighted average F1-score of 0.912, an AUC-ROC of 0.986, and an average precision score of 0.919. Additionally, the GBFS workflow underpinned a regression model that predicted Tc values with an R2 of 0.945, an MAE of 3.54 K, and an RMSE of 6.57 K on a test set obtained via random splitting. Further exploration was conducted through out-of-sample Tc predictions, particularly those exceeding the liquid nitrogen temperature, and out-of-distribution predictions for (Ca1−xLax)FeAs2 based on varying lanthanum content. The outcome of our study underscores the significance of systematic feature analysis and selection in enhancing predictive model performance, offering various advantages over models that rely primarily on algorithmic complexity. This research not only advances the field of superconductivity but also sets a precedent for the application of machine learning in materials science. 1. INTRODUCTION Superconductivity is a phenomenon in certain materials that is made distinctive by the complete disappearance of electrical resistance and the expulsion of magnetic fields, facilitated by the Meissner effect. 1,2 Such materials are termed superconductors. Unlike conventional metallic conductors, whose resistance gradually decreases as the temperature approaches absolute zero, superconductors transition abruptly to a superconducting state, enabling the lossless conduction of electricity below a certain critical temperature (Tc). This distinctive behavior underpins the significant potential applications of superconductors across various fields. However, the existing phenomenological theories of superconductivity are found to be insufficient, primarily due to the considerable theoretical and experimental challenges associated with understanding the intrinsic relationship between superconductivity and both the chemical composition and structural features of these materials. Notably, the mechanisms underlying high-temperature superconductivity in cuprates3,4􀀁a class of compounds characterized by crystal planes composed of copper and oxygen atoms􀀁and iron-based families5−7 have yet to be fully comprehended. This shortfall highlights a gap in the prevailing theoretical frameworks, necess

FAQs

Why is predicting critical temperature (Tc) difficult for superconducting materials?

Tc prediction is challenging because superconducting properties depend on intricate relationships among chemical composition and structural characteristics of materials.

What is the GBFS workflow used in the study?

GBFS is a machine-learningbased workflow that combines exploratory data analysis, statistical evaluation, and multicollinearity reduction to select highly relevant features from chemical composition–derived descriptors.

How did the models perform for classifying and predicting Tc?

The classification model distinguished compositions likely to show Tc > 10 K with a weighted F1-score of 0.912 and AUC-ROC of 0.986. The regression model predicted Tc with R2 = 0.945 and errors including MAE 3.54 K and RMSE 6.57 K on a randomly split test set.

Aug 4, 2026

Upload by Maeve