Predicting critical temperature of superconductors via machine learning methods

Date

Sep 15, 2025

Time

2:00 PM - 3:00 PM

Speaker

Ihor Svynarskyi

Affiliation

Kyiv Academic University, Kyiv

Language

en

Main Topic

Materialien

Host

Grit Rötzer

Description

Superconductivity has been studied for over a century already, yet understanding the nature of high-temperature superconductivity remains rather challenging. Using the 3DSC database, which incorporates information on crystal structure of superconductors through special descriptors, and integrating a fingerprint of electronic density of states into it, we address this problem with machine learning techniques. We explore dimensionality reduction via Principal Component Analysis, unsupervised clustering of superconductors and finally prediction of the critical temperature with regression models, namely K-nearest neighbours, Random Forest, and Gradient Boosting. Despite the limited data (fewer than 4000 compounds, including around 900 HTSC), our models achieve competitive results, leading the way to the search for new superconductors. .

Links

• Seminars at the IFW

Last modified: Sep 15, 2025, 7:38:07 AM