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Keywords: Machine learning, state-of-the-art, materials design, autonomous labs, data management

INTRODUCTION
The International Workshop on Data-Driven Computational and Theoretical Materials Design (DCTMD)
was held from October 9 to 13, 2024, in Shanghai, amidst the buzz surrounding the announcements of the
Nobel Prizes in Physics and Chemistry. The workshop aimed to gather leading scientists and researchers
from around the world, representing various aspects of data-driven artificial intelligence (AI) methodologies
and applications in materials design, to facilitate the exchange of the latest research and stimulate
discussion. There were 191 participants from 11 different countries, representing academia and industry, at
various career levels, providing a diverse range of perspectives. The focus areas were chosen to highlight
innovative approaches and technologies in materials research today, including:
• Data management and stewardship for materials
• AI for materials design
• AI/autonomous/self-driving/automatic materials lab
• High-throughput computational and experimental materials design
• Advanced computing for materials design

As part of the workshop, a panel discussion titled “Unlocking the AI Future of Materials Science” was held -
[1]
available in full on Koushare - to disseminate the state-of-the-art of AI/ML in materials science and
explore directions for the future. The following seed questions were posed to the panel to stimulate
discussion:
- What has been the greatest success of AI/ML in the sciences?
- With growing skepticism about the validity of AI claims and the issue of hallucinations in large language
models (LLMs), should we be putting standards in place to determine when AI claims can be taken
seriously? Especially considering that robust scientific theories and models have clearly defined domains of
validity (e.g., classical mechanics vs. quantum mechanics), and data come with error bars. What, then, can
we say about machine learning models?
- AI/ML in materials science is undoubtedly data-driven, but compared to some other disciplines, we are
not yet truly doing Big Data. What should the community be doing to ensure the integrity and accessibility
of data?
- Looking to the future, what will be the next breakthrough area for AI in materials science - or, failing
that, what problem would you most like to see AI solve?

These questions underpinned the many talks of the conference, especially in judging metrics for success and
how to achieve them. The insights gained from the panel discussion, talks and accompanying conversations
are given below.

DISCUSSION
The DCTDMD workshop indeed managed to cover a wide range of topics and themes in materials research
as outlined by the focus areas above, and these could be roughly categorized across the 7 plenary talks, 25
invited talks, 14 contributed talks, and 29 posters as follows:
- Machine learning for materials design
- Method development
- Machine learning interatomic potentials (MLIP)