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Peer-reviewedLicence not statedNature Computational Science

Robust generative transition-state models for unseen chemistry

  • Samir Darouich1,2,3
  • Jacob Toney1
  • Weiliang Luo1,4
  • Johannes Kästner3
  • Mathias Niepert2
  • Heather J. Kulik5,6
6 affiliations
  1. 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
  2. 2Institute for Artificial Intelligence, University of Stuttgart, Stuttgart, Germany
  3. 3Institute for Theoretical Chemistry, University of Stuttgart, Stuttgart, Germany
  4. 4Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
  5. 5Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected]
  6. 6Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected]
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Cite

BibTeXRIS
BibTeX
@article{darouich2026robust,
  title = {{Robust generative transition-state models for unseen chemistry}},
  author = {Samir Darouich and Jacob Toney and Weiliang Luo and Johannes Kästner and Mathias Niepert and Heather J. Kulik},
  journal = {Nature Computational Science},
  year = {2026},
  month = aug,
  doi = {10.1038/s43588-026-01034-5},
  url = {https://doi.org/10.48550/arxiv.2601.16469},
}
RIS
TY  - JOUR
TI  - Robust generative transition-state models for unseen chemistry
AU  - Samir Darouich
AU  - Jacob Toney
AU  - Weiliang Luo
AU  - Johannes Kästner
AU  - Mathias Niepert
AU  - Heather J. Kulik
PY  - 2026
DA  - 2026/08/12
JO  - Nature Computational Science
DO  - 10.1038/s43588-026-01034-5
UR  - https://doi.org/10.48550/arxiv.2601.16469
SN  - 2662-8457
ER  -