Superconductor Discovery in the Emerging Paradigm of Materials Informatics

The past 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 gigapascals. These
discoveries were strongly driven by Migdal–Éliashberg
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 work, we review the computationally
driven discoveries and the recent developments in the field from various
essential aspects, including the theoretically based, computationally
based, and, specifically, artificial intelligence/machine learning
(AI/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 their infancy
for good reasons. However, there are opportunities in which these
approaches can be further developed and integrated in concerted efforts,
in which AI/ML approaches could play more important roles.