moTSart: accelerating automated transition state search with generative models in a low-data regime.

Galustian L, Karwounopoulos J, Demuth T, De Landsheere J, Mark K, Kovar MP, Zamyatin A, Svatunek D, Heid E

DOI: 10.1039/d6dd00259e
Published: 2026 Sep 11
Container: Digital discovery
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1039/d6dd00259e,
  title = {moTSart: accelerating automated transition state search with generative models in a low-data regime.},
  author = {Galustian L and Karwounopoulos J and Demuth T and De Landsheere J and Mark K and Kovar MP and Zamyatin A and Svatunek D and Heid E},
  year = {2026},
  journal = {Digital discovery},
  doi = {10.1039/d6dd00259e},
  url = {https://doi.org/10.1039/d6dd00259e}
}

RIS

TY  - JOUR
TI  - moTSart: accelerating automated transition state search with generative models in a low-data regime.
AU  - Galustian L
AU  - Karwounopoulos J
AU  - Demuth T
AU  - De Landsheere J
AU  - Mark K
AU  - Kovar MP
AU  - Zamyatin A
AU  - Svatunek D
AU  - Heid E
PY  - 2026
JO  - Digital discovery
DO  - 10.1039/d6dd00259e
UR  - https://doi.org/10.1039/d6dd00259e
ER  -

APA

L, G., J, K., T, D., J, D. L., K, M., MP, K., A, Z., D, S., & E, H. (2026). moTSart: accelerating automated transition state search with generative models in a low-data regime.. Digital discovery. https://doi.org/10.1039/d6dd00259e

Source records

pubmed · retrieved 2026-09-24T21:47:14.450Z