An automatic approach to explore multi-reaction mechanism for medium-sized bimolecular reactions via collision dynamics simulations and transition state searches
Qinghai Cui, Jiawei Peng, Chao Xu, Zhenggang Lan

TL;DR
This paper introduces an automated computational approach combining collision dynamics and transition state searches to efficiently explore complex multi-reaction mechanisms in medium-sized bimolecular systems, demonstrated on environmental reactions.
Contribution
The method integrates BOMD simulations, prescreening tricks, and hidden Markov models to automatically identify reaction pathways, reducing computational cost and complexity.
Findings
Successfully applied to penicillin G reactions
Efficiently identified multiple reaction pathways
Reduced computational cost significantly
Abstract
We develop a broadly-applicable computational method for the automatic exploration of the bimolecular multi-reaction mechanism. The current methodology mainly involves the high-energy Born-Oppenheimer molecular dynamics (BOMD) simulation and the successive reaction pathway construction. Several computational tricks are introduced, which include the selection of the reactive regions based on the electronic-structure calculations and the employment of the virtual collision-dynamics simulations with monitoring atomic distance before BOMD. These prescreening steps largely reduce the number of trajectories in the BOMD simulations and significantly save computational cost. The hidden Markov model combined with modified atomic connectivity matrix is taken for the detection of reaction events in each BOMD trajectory. Starting from several geometries close to reaction events, the further…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpectroscopy and Quantum Chemical Studies · Computational Drug Discovery Methods · Machine Learning in Materials Science
