Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning

TL;DR

This study combines BCS-inspired screening, density functional theory, and deep learning to efficiently identify and predict properties of potential high-temperature superconductors from a large materials database.

Contribution

It introduces a multi-step workflow integrating pre-screening, DFT calculations, and deep learning models to accelerate superconductor discovery.

Findings

Identified 105 stable materials with Tc > 5 K.

Deep learning models predict superconducting properties faster than first principles methods.

Predicting the Eliashberg function improves model accuracy for Tc prediction.

Abstract

We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen Cooper Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states. Next, we perform electron-phonon coupling calculations for 1058 of them to establish a large and systematic database of BCS superconducting properties. Using the McMillan-Allen-Dynes formula, we identify 105 dynamically stable materials with transition temperatures, Tc>5 K. Additionally, we analyze trends in our dataset and individual materials including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, and TaC. We demonstrate that deep-learning(DL) models can predict superconductor properties faster than direct first principles computations. Notably, we find that by predicting the Eliashberg function as an intermediate quantity, we can improve model performance versus a direct DL prediction of Tc. We apply the trained models on the crystallographic open database and pre-screen candidates for further DFT calculations.