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Kobayashi et al. J. Mater. Inf. 2025, 5, 50 Journal of
DOI: 10.20517/jmi.2025.44
Materials Informatics

Conference Report Open Access

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

5
3,4
6,7
8
2
Rika Kobayashi 1,* , Roger D. Amos , T. Daniel Crawford , Hongxia Hao , Yi Liu , Turab Lookman ,
9
10
11
Rampi Ramprasad , Matthias Scheffler , Hong Wang , Tong-Yi Zhang 6
1
Supercomputer Facility, Australian National University, Canberra ACT 2601, Australia.
2
University of NSW, Canberra ACT 2600, Australia.
3
Department of Chemistry, Virginia Tech, Blacksburg, VA 24061, USA.
4
Molecular Sciences Software Institute, Blacksburg, VA 24060, USA.
5
Microsoft Research AI for Science, Shanghai, China.
6
Materials Genome Institute, Shanghai University, Shanghai 200444, China.
7
Department of Physics, Shanghai University, Shanghai 200444, China.
8
AiMaterials Research LLC, USA.
9
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
10
The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, Berlin 14195, Germany.
11
Materials Genome Initiative Center & School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai
200240, China.
* Correspondence to: Dr. Rika Kobayashi, Supercomputer Facility, Australian National University, Canberra ACT 2601, Australia.
E-mail: Rika.Kobayashi@anu.edu.au
How to cite this article: Kobayashi, R.; Amos, R. D.; Crawford, T. D.; Hao, H.; Liu, Y.; Lookman, T.; Ramprasad, R.; Scheffler, M.;
Wang, H.; Zhang, T. Y. Unlocking the future of materials science: key insights from the DCTMD workshop. J. Mater. Inf. 2025, 5,
50. https://dx.doi.org/10.20517/jmi.2025.44
Received: 1 Jun 2025 First Decision: 7 Jul 2025 Revised: 14 Jul 2025 Accepted: 22 Jul 2025 Published: 4 Nov 2025
Academic Editor: William Yi Wang Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang
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.

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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