ChemXDyn: Dynamics-Informed Species and Reaction Detection Methodology from Atomistic Simulations
, which is consistent with established mechanisms yet is often fragmented by threshold-based approaches. By linking atomistic dynamics to chemically consistent reaction identification, ChemXDyn provides a transferable foundation for MD-derived reaction networks and kinetics, with potential utility spanning combustion, heterogeneous catalysis, plasma chemistry, and electrochemical reaction environments.
Authors
- Konduri Aditya (ORCID: https://orcid.org/0000-0003-2502-2110)
- E. Arunan (ORCID: https://orcid.org/0000-0001-9669-4307)
- Phani Motamarri (ORCID: https://orcid.org/0000-0003-3160-7334)
- Raj Maddipati (ORCID: https://orcid.org/0000-0002-9280-5265)
- Dhruthi Boddapati
Institutions
- Indian Institute of Science Bangalore (IN)
Publication Details
- Journal
- Journal of Chemical Theory and Computation
- Published
- 2026-04-17
- DOI
- https://doi.org/10.1021/acs.jctc.6c00242
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Science and Engineering Research Board
- Google Research