AI-Driven Discovery of High-Temperature Superconductors via Materials Genome Initiative and High-Throughput Screening

TL;DR

This paper integrates AI and high-throughput screening within the Materials Genome Initiative framework to identify key elements and predict high-temperature superconductors, significantly advancing materials discovery methods.

Contribution

It introduces a novel neural network architecture and a comprehensive dataset, enhancing the accuracy of superconductor prediction and uncovering crucial elemental predictors like Pu and H.

Findings

CNN and LSTM models accurately predict Tc and key elements.

Pu and H elements are significant predictors of high Tc.

The new neural network architecture improves prediction performance.

Abstract

Inspired by nature, this study employs the Materials Genome Initiative to identify key components of HTSC superconductors. Integrating AI with high-throughput screening, we uncover crucial superconducting "genes". Through HTS techniques and advanced machine learning models, we demonstrate that Functional Convolutional Neural Networks (CNNs) ensure accurate extrapolation of potential compounds. Leveraging extensive datasets from the ICSD, the Materials Project and COD, our implemented HTS pipeline classifies superconductors, with CNN and long short-term memory (LSTM) models predicting Tc and their foundational elements. We address the scarcity of non-superconducting material data by compiling a dataset of 53,196 non-superconducting materials (DataG Non-Sc) and introduce a novel neural network architecture using Functional API for improved prediction, offering a powerful tool for future superconductor discovery. Our findings underscore the transformative potential of combining HTS with AI-driven models in advancing HTSC materials, highlighting Pu and H elements (with Tc nearly 100 K) as significant predictors of high-temperature superconductivity, suggesting their role as a crucial gene in these materials.