Autonomous discovery of reaction networks from active-learning-driven reactive molecular dynamics
- 1Institute for Physical Chemistry, Heinrich Heine University Düsseldorf, Universitätsstraße 1, Düsseldorf, North Rhine-Westphalia, Germany, 40225
Abstract
Reaction networks underlie complex chemical systems such as combustion, catalysis, and atmospheric chemistry and can comprise thousands of elementary reactions. Ab initio nanoreactor molecular dynamics explores such networks efficiently by accelerated sampling of the potential energy surface, but computational cost limits how far it can sample. This work presents a transferable methodology coupling nanoreactor molecular dynamics with active learning to autonomously generate reaction networks from starting molecules. An agentdeveloped parser extracts elementary reactions from trajectories, and a large-language-model coding agent improves transition-state search strategies. Applied to the atmospheric oxidation of dimethyl sulfide, the methodology yields 980 nanoseconds of simulation, requiring only 290,195 quantum-chemical calculations. Initializing reaction discovery from already discovered species drastically speeds up reaction network exploration leading to 13,271 elementary reactions and 6,995 species. The reaction network covers most of the established mechanism but also unveils novel species such as an isomer of the key intermediate HPMTF.
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BibTeX
@misc{meissner2026autonomous,
title = {{Autonomous discovery of reaction networks from active-learning-driven reactive molecular dynamics}},
author = {Jan A. Meissner and Jan Meisner},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15011187/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011187/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Autonomous discovery of reaction networks from active-learning-driven reactive molecular dynamics
AU - Jan A. Meissner
AU - Jan Meisner
PY - 2026
DA - 2026/10/28
PB - ChemRxiv
DO - 10.26434/chemrxiv.15011187/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011187/v1
AB - Reaction networks underlie complex chemical systems such as combustion, catalysis, and atmospheric chemistry and can comprise thousands of elementary reactions. Ab initio nanoreactor molecular dynamics explores such networks efficiently by accelerated sampling of the potential energy surface, but computational cost limits how far it can sample. This work presents a transferable methodology coupling nanoreactor molecular dynamics with active learning to autonomously generate reaction networks from starting molecules. An agentdeveloped parser extracts elementary reactions from trajectories, and a large-language-model coding agent improves transition-state search strategies. Applied to the atmospheric oxidation of dimethyl sulfide, the methodology yields 980 nanoseconds of simulation, requiring only 290,195 quantum-chemical calculations. Initializing reaction discovery from already discovered species drastically speeds up reaction network exploration leading to 13,271 elementary reactions and 6,995 species. The reaction network covers most of the established mechanism but also unveils novel species such as an isomer of the key intermediate HPMTF.
ER -