Foundation models for materials discovery – current state and future directions
Abstract Large language models, commonly known as LLMs, are showing promise in tacking some of the most complex tasks in AI. In this perspective, we review the wider field of foundation models—of which LLMs are a component—and their application to the field of materials discovery. In addition to the current state of the art—including applications to property prediction, synthesis planning and molecular generation—we also take a look to the future, and posit how new methods of data capture, and indeed modalities of data, will influence the direction of this emerging field.
Authors
- Lucas Morin (ORCID: https://orcid.org/0000-0002-5829-5118)
- Matteo Manica (ORCID: https://orcid.org/0000-0002-8872-0269)
- Peter Staar (ORCID: https://orcid.org/0000-0002-8088-0823)
- Patrick Ruch (ORCID: https://orcid.org/0000-0002-3374-2962)
- Teodoro Laino (ORCID: https://orcid.org/0000-0001-8717-0456)
- Edward O. Pyzer‐Knapp (ORCID: https://orcid.org/0000-0002-8232-8282)
- John R. Smith
- Alessandro Curioni
Institutions
- IBM (United States) (US)
- IBM Research - Thomas J. Watson Research Center (US)
- IBM Research - Zurich (CH)
- New College (GB)
Publication Details
- Journal
- npj Computational Materials
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1038/s41524-025-01538-0
- Citations
- 48
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 24.85