Targeted TPS Shooting Using Computer Vision to Generate Ensemble of Trajectories.

Area of Science:

• Computational Chemistry
• Chemical Physics
• Machine Learning Applications

Background:

• Transition Path Sampling (TPS) is crucial for studying chemical reaction mechanisms.
• Automating trajectory analysis in TPS can significantly improve efficiency.
• Computer vision techniques offer novel ways to interpret complex simulation data.

Purpose of the Study:

• To develop and validate an automated TPS procedure using a 3D convolutional neural network (CNN).
• To enhance the efficiency of generating and analyzing chemical reaction trajectories.
• To apply the automated method to the Morita-Bayliss-Hillman (MBH) reaction.

Main Methods:

• Implementation of a transition path sampling (TPS) procedure.
• Integration of a 3D convolutional neural network (CNN) for trajectory classification.
• Utilizing geometrical configuration data to optimize TPS parameters.
• Application to the rate-limiting step of the Morita-Bayliss-Hillman (MBH) reaction.

Main Results:

• The 3D CNN effectively sorts TPS trajectory slices into reactant or product states.
• The method allows for automatic acceptance/rejection of generated trajectories.
• Statistics derived from geometrical configurations aid in optimizing the shooting range and acceptance rate.
• Successful collection of transition paths for the MBH reaction.

Conclusions:

• The automated 3D CNN TPS technique provides an efficient and accurate approach for studying chemical transformations.
• This method can accelerate the analysis of reaction dynamics and mechanism elucidation.
• The integration of computer vision in computational chemistry holds significant promise.