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DOE OSTI · 2562956

Challenges and opportunities for machine learning potentials in transition path sampling: alanine dipeptide and azobenzene studies

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)·Li, Wei [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA]·Lubbers, Nicholas [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000290019973)·Nebgen, Benjamin [Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000153103263)·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)·Li, Ying Wai [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000301248262)
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Abstract

ML potentials enable fast TPS simulations ( e.g. , for alanine dipeptide) but may fail for complex cases like azobenzene isomerization. Adequate reference data and domain expertise are crucial for selecting test trajectories.

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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), Li, Wei [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA], Lubbers, Nicholas [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000290019973), Nebgen, Benjamin [Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000153103263), 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), Li, Ying Wai [Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA] (ORCID:0000000301248262). 2025-05-14. Challenges and opportunities for machine learning potentials in transition path sampling: alanine dipeptide and azobenzene studies. https://doi.org/10.1039/d4dd00265b

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