Challenges and opportunities for machine learning potentials in transition path sampling: alanine dipeptide and azobenzene studies
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.
Authors
- Wei Li (ORCID: https://orcid.org/0000-0002-0789-0320)
- Ying Wai Li (ORCID: https://orcid.org/0000-0003-0124-8262)
- Nikita Fedik (ORCID: https://orcid.org/0000-0002-6869-7268)
Institutions
- Los Alamos National Laboratory (US)
- Center for Integrated Nanotechnologies (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4dd00265b
- Citations
- 5
- Primary Topic
- Chemical Synthesis and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 4.03