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- Advanced computing
- Infrastructure and standards
- LLMs
- Autonomous labs

and represented in Figure 1 as a pie chart. This is only a rough breakdown, as some talks covered multiple
topics - though to varying extents - but it provides a good representation of where efforts are currently
focused in AI/ML in materials science. Additionally, the categories above were chosen to better highlight
certain points. By far, the majority of presentations dealt with the application of ML techniques - analyzing
data to make predictions through supervised learning. There was also significant work in Method
Development, focusing on building better models to more accurately represent materials and predict their
properties. This was particularly evident in the plenary and invited talks, which were skewed by the
selection of speakers recognized as pioneers in advancing the field. Furthermore, the MLIP category
encompasses these two areas - method development and application to materials design - but has been
separated to highlight the popularity of this approach. Indeed, this is the methodology underpinning the
AlphaFold work, which won the Nobel Prize in Chemistry. There were a few talks on robotic labs, providing
impressive evidence that robotic synthesis can be more systematically reproducible than when done by
humans. Surprisingly, there was not a larger representation of talks on LLMs, especially given the internet’s
perception that ChatGPT and DeepSeek are taking over the world. While LLMs were frequently mentioned
in many talks, few concrete results were presented, reflecting the hype and showing that the use of LLMs in
materials science is still in its early days. Notably, however, is the category labeled Advanced Computing.
These talks focused on conventional computational materials science - without ML - and even in many of
the ML talks, conventional computational materials science played a significant role. In the context of this
audience, the findings of the panel questions are summarized in detail as follows.

Successes of AI/ML in materials science
Shortly before the workshop, the announcement of the Nobel Prizes in physics and chemistry was made.
The prize for physics went to John Hopfield and Geoffrey Hinton for “foundational discoveries and
inventions that enable machine learning with artificial neural networks” (https://www.nobelprize.org/
prizes/physics/). The chemistry prize was awarded to David Baker, Demis Hassabis and John Jumper for
“computational protein design” and “protein structure prediction” (https://www.nobelprize.org/prizes/
chemistry/). For materials science the closest equivalent to breakthrough science was generally speculated to
be the rise of high throughput materials discovery and autonomous labs (to be discussed in the next
section). However, further consideration raised the question, “What does success look like?” The consensus
was that there has yet to be an AlphaFold-equivalent breakthrough moment in materials science, but
numerous small successes have demonstrated that AI/ML can be a useful tool when used in conjunction
with other methods. In particular, as attested by the prevalence of studies, machine learning force fields are
popular - indeed, they were part of the AlphaFold breakthrough. AI-driven materials design success stories
are beginning to emerge in many areas of materials science, such as the design of application-specific
[2]
practical polymeric materials or the development of a tolerance factor to predict the stability of not yet
synthesized perovskites . Feature engineering, integrating digital materials representations, has provided
[3]
insight into determining the capability and accuracy of material property prediction.

Autonomous labs
One of the biggest talking points in recent years was spurred by a pair of Nature papers on the AI
[4,5]
discovery of novel materials and the use of autonomous labs. In the first paper , machine learning
[4]
techniques claim to have “discovered” 2.2 million novel structures, heralded as a breakthrough in materials
discovery. Coupled with a workflow where these novel compounds can go straight to synthesis through