Multimodal foundation models for material property prediction and discovery
Publisher of over 50 scientific journals across the life, physical, earth, and health sciences, both independently and in partnership with scientific societies including Cell, Neuron, Immunity, Current Biology, AJHG, and the Trends Journals.
Authors
- Thomas Christensen (ORCID: https://orcid.org/0000-0002-9131-2717)
- Andrew Ma (ORCID: https://orcid.org/0000-0001-6403-7740)
- Charlotte Loh (ORCID: https://orcid.org/0000-0003-4587-808X)
- Peter Y. Lu (ORCID: https://orcid.org/0000-0001-6183-5237)
- Samuel Kim (ORCID: https://orcid.org/0000-0001-8248-2346)
- Rumen Dangovski (ORCID: https://orcid.org/0000-0003-0814-8934)
- Marin Soljačić (ORCID: https://orcid.org/0000-0002-7184-5831)
- Zhuo Chen (ORCID: https://orcid.org/0000-0001-5370-2688)
- Ali Ghorashi (ORCID: https://orcid.org/0000-0002-5884-1756)
- Viggo Moro
Institutions
- Johns Hopkins University Applied Physics Laboratory (US)
- University of Chicago (US)
- Massachusetts Institute of Technology (US)
- Technical University of Denmark (DK)
Publication Details
- Journal
- Newton
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1016/j.newton.2025.100016
- Citations
- 21
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 10.87
Funders
- Villum Fonden
- DSO National Laboratories - Singapore
- Air Force Office of Scientific Research
- Air Force Research Laboratory
- DEVCOM Army Research Laboratory