Dynamics Sampling in Transition Pathway Space.

Area of Science:

• Computational Chemistry
• Chemical Physics
• Materials Science

Background:

• Minimum energy pathways (MEPs) are crucial for understanding transitions between molecular states on potential energy surfaces (PES).
• Existing chain-of-states methods efficiently calculate single MEPs but may miss alternative pathways.
• Identifying multiple pathways is essential for a comprehensive understanding of chemical processes.

Purpose of the Study:

• To develop an enhanced sampling method for exploring multiple transition pathways on a PES.
• To facilitate the discovery of additional minima and their associated transition pathways.
• To improve the exploration of high-energy regions, including transition states, on a PES.

Main Methods:

• Developed the direct pathway dynamics sampling (DPDS) method, integrating molecular dynamics simulations within a chain-of-states framework.
• DPDS directly samples transition pathway space on the PES without requiring predefined reaction coordinates.
• Simulations are regulated by parameters controlling inter-state distance and pathway smoothness.

Main Results:

• DPDS effectively samples multiple pathways connecting stable states and identifies additional minima.
• The method efficiently explores high-energy regions of the PES, crucial for reaction dynamics.
• Demonstrated the efficiency of DPDS through three illustrative examples.

Conclusions:

• DPDS is a powerful tool for comprehensive exploration of transition pathway space on PES.
• The method overcomes limitations of previous approaches by enabling the discovery of multiple pathways and minima.
• DPDS significantly enhances the understanding of complex molecular transitions and reaction mechanisms.