Superconductor discovery in the emerging paradigm of Materials Informatics

TL;DR

This paper reviews recent advances in computational and AI/ML methods for discovering superconductors, emphasizing the integration of materials informatics to address challenges in high-pressure hydride superconductor predictions.

Contribution

It provides a comprehensive overview of computational and AI/ML approaches in superconductor discovery, highlighting opportunities for further development within materials informatics.

Findings

AI/ML methods are still in early stages for superconductor discovery

Computational predictions have led to experimental syntheses of high-pressure hydrides

Challenges remain in integrating AI/ML with traditional computational approaches

Abstract

The last two decades have witnessed a tremendous number of computational predictions of hydride-based (phonon-mediated) superconductors, mostly at extremely high pressures, i.e., hundreds of GPa. These discoveries were heavily driven by Migdal-\'{E}liashberg theory (and its first-principles computational implementations) for electron-phonon interactions, the key concept of phonon-mediated superconductivity. Dozens of predictions were experimentally synthesized and characterized, triggering not only enormous excitement in the community but also some debates. In this Article, we review the computational-driven discoveries and the recent developments in the field from various essential aspects, including the theoretical, computational, and, specifically, artificial intelligence (AI)/machine learning (ML) based approaches emerging within the paradigm of materials informatics. While challenges and critical gaps can be found in all of these approaches, AI/ML efforts specifically remain in its infant stage for good reasons. However, opportunities exist when these approaches can be further developed and integrated in concerted efforts, in which AI/ML approaches could play more important roles.