Exploration of Minimum Energy Paths Through Feature‐Vector Force‐Driven Reaction Coordinate Exploration Approach

Predicting reaction minimum-energy paths (MEPs) and locating transition states (TSs) on the potential energy surface (PES) are challenging problems. The high scaling of electronic structure methods and the computational expense of higher-energy derivative calculations limit the exploration of reaction mechanisms to smaller molecular systems. Therefore, tracing the MEPs along the intrinsic reaction coordinate is formidable. In this work, we have benchmarked a rapid method, the Feature-vector Force-driven Reaction Coordinate Exploration (FFoRCE) approach, for generating MEPs connecting the reactant to the product on the PES. This two-step approach involves a reaction-coordinate navigator that generates the initial path from reactant to product, followed by local relaxations of each structure along the sampled path. The reaction coordinate navigator transforms the coordinate space into a set of features and assigns each structure along the reaction coordinate path a unique value. The reaction coordinate navigator generates an initial path iteratively, followed by a symmetry function-constrained geometry optimization. In this process, the MEP structures are generated by minimizing the total energy along the TS. In this work, the performance of the FFoRCE approach is benchmarked on 10 elementary gas-phase organic reactions by tracing MEPs and locating TSs. Furthermore, we compared their geometries, activation energies, and vibrational frequencies with those from whole-molecule calculations. A fair agreement between them shows that the FFoRCE approach reproduces the MEP and locates the TS as accurately as DFT methods, and it is promising for future applications to complex reactions.

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Publication Details

Journal
Journal of Computational Chemistry
Published
2026-07-24
DOI
https://doi.org/10.1002/jcc.70465
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Exploration of Minimum Energy Paths Through Feature‐Vector Force‐Driven Reaction Coordinate Exploration Approach

Siva Chiriki, K. V. Jovan Jose, Dudam Praveen
Journal of Computational Chemistry
Machine Learning in Materials Science
article

Exploration of Minimum Energy Paths Through Feature‐Vector Force‐Driven Reaction Coordinate Exploration Approach

Siva Chiriki, K. V. Jovan Jose, Dudam Praveen
article en

Abstract

Predicting reaction minimum-energy paths (MEPs) and locating transition states (TSs) on the potential energy surface (PES) are challenging problems. The high scaling of electronic structure methods and the computational expense of higher-energy derivative calculations limit the exploration of reaction mechanisms to smaller molecular systems. Therefore, tracing the MEPs along the intrinsic reaction coordinate is formidable. In this work, we have benchmarked a rapid method, the Feature-vector Force-driven Reaction Coordinate Exploration (FFoRCE) approach, for generating MEPs connecting the reactant to the product on the PES. This two-step approach involves a reaction-coordinate navigator that generates the initial path from reactant to product, followed by local relaxations of each structure along the sampled path. The reaction coordinate navigator transforms the coordinate space into a set of features and assigns each structure along the reaction coordinate path a unique value. The reaction coordinate navigator generates an initial path iteratively, followed by a symmetry function-constrained geometry optimization. In this process, the MEP structures are generated by minimizing the total energy along the TS. In this work, the performance of the FFoRCE approach is benchmarked on 10 elementary gas-phase organic reactions by tracing MEPs and locating TSs. Furthermore, we compared their geometries, activation energies, and vibrational frequencies with those from whole-molecule calculations. A fair agreement between them shows that the FFoRCE approach reproduces the MEP and locates the TS as accurately as DFT methods, and it is promising for future applications to complex reactions.

Journal of Computational ChemistryVol. 47(20)
University of Hyderabad (IN)
Affordable and clean energy
Openalex Percentile: Top 19%
Machine Learning in Materials Science
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