@misc{indiciae0c9ff1fd60a7,
  title = {Challenges and opportunities for machine learning potentials in transition path sampling: alanine dipeptide and azobenzene studies},
  author = {Fedik, Nikita [Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA, Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000268697268) and Li, Wei [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] and Lubbers, Nicholas [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000290019973) and Nebgen, Benjamin [Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000153103263) and Tretiak, Sergei [Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA, Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA, Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, NM, USA] (ORCID:0000000155473647) and Li, Ying Wai [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000301248262)},
  year = {2025},
  doi = {10.1039/d4dd00265b},
  url = {https://www.osti.gov/biblio/2562956},
  note = {Source identifier: 2562956}
}