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.

中文翻译：

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新兴材料信息学范式中的超导体发现


在过去的二十年里，见证了大量氢化物基（声子介导的）超导体的计算预测，这些预测大多是在极高的压力下，即数百吉帕。这些发现受到 Migdal-Éliashberg 理论（及其第一性原理计算实现）的强烈推动，该理论是声子介导的超导性的关键概念。数十个预测被实验综合和表征，不仅在社区中引发了巨大的兴奋，还引发了一些辩论。在这项工作中，我们从各个重要方面回顾了该领域的计算驱动发现和最新发展，包括材料信息学范式中出现的基于理论、基于计算的方法，特别是基于人工智能/机器学习 （AI/ML） 的方法。虽然在所有这些方法中都可以发现挑战和关键差距，但 AI/ML 工作仍然处于起步阶段，这是有充分理由的。然而，这些方法有机会得到进一步发展，并整合为协同工作，其中 AI/ML 方法可以发挥更重要的作用。