Topic: “Unlocking the AI Future of Materials Science”: Selected Papers from the International Workshop on Data-driven Computational and Theoretical Materials Design (DCTMD)

The rapidly evolving domain of data-driven science marks a transformative shift across various scientific disciplines, establishing itself as a cornerstone alongside traditional pillars such as experimentation, theoretical analysis, and computation. At the heart of this paradigm shift is Artificial Intelligence (AI), which offers unparalleled opportunities for unveiling the intricate relationships between composition, structure, and the properties or performance of materials. This revolution paves the way for accelerated materials discovery and innovation, propelling the field into a new era of research and development. In response to the burgeoning impact of AI on materials science, the International Workshop on Data-Driven Computational and Theoretical Materials Design (DCTMD2024) emerged as a pivotal event aimed at “Unlocking the AI Future of Materials Science”, which was held successfully in Shanghai, China from October 9-13, 2024. Nearly 200 researchers from more than 20 countries attended and there were 46 talks and 36 posters covering various aspects of AI-assisted computational and experimental materials design. This Special Issue aims to capture and build on the lively discussions and insights gained from the workshop in the diverse areas of AI for Materials Design from data management to advanced computing and autonomous experiment laboratories.