Flat-Bottom Elastic Network Model for Generating Improved Plausible Reaction Paths

Rapid
generation of a plausible reaction path connecting a given
reactant and product in advance is crucial for the efficient computation
of precise reaction paths or transition states. We propose a computationally
efficient potential energy based on the molecular structure to generate
such paths. This potential energy has a flat bottom consisting of
structures without atomic collisions while preserving nonreactive
chemical bonds, bond angles, and partial planar structures. By combining
this potential energy with the direct MaxFlux method, a recently developed
reaction-path/transition-state
search method, we can find the shortest plausible path passing within
the bottom. Numerical results show that this combination yields lower
energy paths compared to the paths obtained by the well-known image-dependent
pair potential. We also theoretically investigate the differences
between these two potential energies. The proposed potential energy
and path generation routine are implemented in our Python version
of the direct MaxFlux method, available on GitHub.