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Two-dimensional RMSD projections for reaction path visualization and validation.

Rohit Goswami1

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Summary
This summary is machine-generated.

We developed a new 2D visualization method to map chemical reaction pathways, overcoming limitations of traditional trajectory plots. This approach aids in comparing different computational chemistry methods for reaction optimization.

Keywords:
Machine learning potentialsNudged Elastic BandReaction dynamicsSaddle PointsVisualization

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Area of Science:

  • Computational Chemistry
  • Chemical Dynamics
  • Data Visualization

Background:

  • Traditional methods for analyzing reaction pathways in computational chemistry often use dimensionality reduction that obscures crucial high-dimensional structural changes.
  • Current analysis methods limit comparisons between different computational approaches, focusing only on calculation counts, time, and final geometries.
  • History dependence in trajectory plotting can hinder objective comparisons of optimization processes.

Purpose of the Study:

  • To introduce a novel framework for visualizing and comparing reaction optimization trajectories from computational chemistry.
  • To enable a more comprehensive analysis of reaction pathways beyond simple geometric or energy comparisons.
  • To facilitate the direct comparison of results from machine-learned potentials and traditional electronic structure calculations.

Main Methods:

  • Mapping reaction trajectories onto a 2D plane using permutation-corrected root mean square deviation (RMSD) from reactant and product configurations.
  • Reconstructing a continuous energy landscape using a gradient-enhanced Gaussian Process with an inverse multiquadric kernel, interpolating sparse optimization data.
  • Utilizing a rotated coordinate frame to decompose the RMSD plane into reaction progress and orthogonal distance.

Main Results:

  • Demonstrated the framework's utility on a cycloaddition reaction, showing comparable energy contours for machine-learned potential and DFT reference despite geometric differences.
  • Validated the visualization method for more complex reactions, including a Grignard rearrangement and a conrotatory bicyclobutane ring opening.
  • The method effectively visualizes basin topologies and allows direct projection of reference calculations onto machine-learned potential landscapes.

Conclusions:

  • The proposed 2D projection and landscape reconstruction method provides a robust tool for analyzing and comparing chemical reaction pathways.
  • This visualization framework overcomes limitations of traditional methods, offering deeper insights into reaction dynamics and computational method performance.
  • The approach facilitates rigorous validation and comparison between data-driven (machine-learned) and first-principles computational chemistry methods.