Steering Langevin Dynamics toward Transition States Using Collective-Variable-Free Resampling
Michael Ketter1, Georg K H Madsen1
1Institute of Materials Chemistry, TU Wien, A-1060 Vienna, Austria.
Journal of Chemical Theory and Computation
|June 1, 2026
Summary
This study adapts the stochastic saddle point dynamics (SSPD) algorithm to efficiently explore chemical reaction pathways. The enhanced method aids in understanding complex molecular processes and reaction dynamics without predefined variables.
Area of Science:
- Computational Chemistry
- Chemical Physics
- Materials Science
Background:
- Understanding chemical reactivity requires exploring potential energy surfaces and transition states.
- Sampling these regions is crucial for elucidating atomic processes.
- Ideally, this sampling should not require predefined collective variables.
Purpose of the Study:
- To adapt and evaluate the stochastic saddle point dynamics (SSPD) algorithm for sampling transition-state regions.
- To demonstrate the algorithm's performance across diverse and complex chemical systems.
- To integrate SSPD with machine learning potentials for enhanced efficiency.
Main Methods:
- Adaptation of the stochastic saddle point dynamics (SSPD) algorithm.
- Constraining configuration space using negative Hessian eigenvalues.
- Application to model potentials, molecular clusters, and surface reactions.
- Integration with machine-learned interatomic potentials.
Main Results:
- Efficient sampling of isomerization and decomposition reactions.
- Successful application to CO dissociation on a Co(001) surface, with and without water.
- Demonstration of SSPD's ability to handle complex systems at finite temperatures.
- Validation of the approach for reactions with unknown energetic or entropic contributions.
Conclusions:
- SSPD provides a versatile framework for sampling transition states in complex systems.
- The adapted algorithm efficiently explores reaction pathways without predefined collective variables.
- This method is valuable for studying chemical reactivity influenced by both energetic and entropic factors.
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