Two-dimensional RMSD projections for reaction path visualization and validation
This work addresses the challenge for computational chemists in comparing and analyzing optimization trajectories, though it is incremental as it builds on existing visualization techniques.
The paper tackles the problem of visualizing and validating reaction paths in computational chemistry by introducing a two-dimensional projection method based on root mean square deviation from reactant and product configurations, which reveals structural rearrangements and convergence issues not visible in standard one-dimensional plots, as validated on a cycloaddition reaction where machine-learned and density functional theory results showed comparable energy contours.
Transition state or minimum energy path finding methods constitute a routine component of the computational chemistry toolkit. Standard analysis involves trajectories conventionally plotted in terms of the relative energy to the initial state against a cumulative displacement variable, or the image number. These dimensional reductions obscure structural rearrangements in high dimensions and may often be trajectory dependent. This precludes the ability to compare optimization trajectories of different methods beyond the number of calculations, time taken, and final saddle geometry. We present a method mapping trajectories onto a two-dimension surface defined by a permutation corrected root mean square deviation from the reactant and product configurations. Energy is represented as an interpolated color-mapped surface constructed from all optimization steps using radial basis functions. This representation highlights optimization trajectories, identifies endpoint basins, and diagnoses convergence concerns invisible in one-dimensional profiles. We validate the framework on a cycloaddition reaction, showing that a machine-learned potential saddle and density functional theory reference lie on comparable energy contours despite geometric displacements.