Large language models as uncertainty-calibrated optimizers for experimental discovery

Bojana Ranković, Ryan-Rhys Griffiths, Philippe Schwaller

Open source

DOI: 10.1038/s42256-026-01283-z
Published: 2026-08-28
Container: Nature Machine Intelligence
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s42256-026-01283-z,
  title = {Large language models as uncertainty-calibrated optimizers for experimental discovery},
  author = {Bojana Ranković and Ryan-Rhys Griffiths and Philippe Schwaller},
  year = {2026},
  journal = {Nature Machine Intelligence},
  doi = {10.1038/s42256-026-01283-z},
  url = {https://doi.org/10.1038/s42256-026-01283-z}
}

RIS

TY  - JOUR
TI  - Large language models as uncertainty-calibrated optimizers for experimental discovery
AU  - Bojana Ranković
AU  - Ryan-Rhys Griffiths
AU  - Philippe Schwaller
PY  - 2026
JO  - Nature Machine Intelligence
DO  - 10.1038/s42256-026-01283-z
UR  - https://doi.org/10.1038/s42256-026-01283-z
ER  -

APA

Ranković, B., Griffiths, R., & Schwaller, P. (2026). Large language models as uncertainty-calibrated optimizers for experimental discovery. Nature Machine Intelligence. https://doi.org/10.1038/s42256-026-01283-z

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crossref · retrieved 2026-11-10T17:57:58.938Z