Unlocking the future of materials science: key insights from the DCTMD workshop

Abstract

The International Workshop on Data-Driven Computational and Theoretical Materials Design was held between October 9-13, 2024, in Shanghai, gathering leading scientists and researchers from around the world, representing various aspects of data-driven AI methodologies and applications in materials design. The topics covered over 46 talks and 29 posters spanned a wide range of the latest advancements, including Machine Learning for Materials Design, Method Development, Machine Learning Interatomic Potentials, Advanced Computing, Infrastructure and Standards, Large Language Models, and Autonomous Labs. As part of the workshop, a panel discussion titled “Unlocking the AI Future of Materials Science” was held to disseminate the state-of-the-art of AI/ML in materials science and consider directions for the future. This report is a synthesis, for this Special Issue, of the panel discussion - drawing on insights gained from the workshop as a whole and surrounding conversations, in particular, the question of what constitutes success.

Keywords

Machine learning / state-of-the-art / materials design / autonomous labs / data management

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