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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Cone-Shaped Constrained Quasi-Newton Method: Efficient and Robust Single-Ended Transition State Optimization

Yinkai Wu¹, Haifeng Wang¹

¹State Key Laboratory of Green Chemical Engineering and Industrial Catalysis, Center for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, Shanghai 200237, People's Republic of China.

Journal of Chemical Theory and Computation

|September 5, 2025

Summary

This summary is machine-generated.

We developed a new Cone-shaped Constrained Quasi-Newton (CCQN) method for finding transition states in catalysis. This efficient approach improves accuracy and reduces reliance on initial guesses for complex reactions.

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Area of Science:

Computational Chemistry
Chemical Kinetics
Materials Science

Background:

Locating transition states is crucial for understanding catalytic reaction mechanisms.
Single-ended methods are efficient but sensitive to initial guesses.
Existing methods struggle with convergence for complex systems.

Purpose of the Study:

To develop a robust and efficient method for transition state (TS) location in heterogeneous catalysis.
To overcome the sensitivity of single-ended methods to initial guesses.
To provide a tool for high-throughput screening and mechanistic studies.

Main Methods:

Introduction of a Cone-shaped Constrained Quasi-Newton (CCQN) method.
Utilizing a cone-shaped constraint to guide searches towards saddle regions.
Employing partitioned rational function optimization for refinement after crossing inflection curves.

Main Results:

CCQN achieved a 93.3% success rate across 150 transition state optimization tasks.
The method required an average of approximately 50 energy-force evaluations.
Demonstrated strong robustness and convergence efficiency, even with poor initial guesses.

Conclusions:

The CCQN method offers a significant improvement for transition state searches.
It combines the efficiency of single-ended methods with reduced sensitivity to initial conditions.
Presents a valuable new tool for computational catalysis research and discovery.