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Topic: “Unlocking the AI Future of Materials Science”: Selected Papers

from the International Workshop on Data-driven Computational and
Theoretical Materials Design (DCTMD)

Guest Editors

Dr. Rika Kobayashi
National Computational Infrastructure, Australian National University, Canberra, ACT, Australia.

Prof. Yi Liu
Materials Genome Institute, Shanghai University, Shanghai, China.

Prof. Matthias Scheffler
The Novel Materials Discovery Laboratory, Fritz Haber Institute, Berlin, Germany.

Prof. Runhai Ouyang
Materials Genome Institute, Shanghai University, Shanghai, China.

Special Topic Introduction

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.

Journal of Materials Informatics I