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[2512.07329] Two-dimensional RMSD projections for reaction path visualization and validation
Summary
This article presents a novel method for visualizing reaction paths in computational chemistry using two-dimensional RMSD projections, enhancing the analysis of optimization trajectories.
Why It Matters
The proposed method addresses limitations in traditional one-dimensional trajectory analysis, allowing for better visualization and comparison of optimization processes in chemical reactions. This advancement is significant for researchers in computational chemistry and machine learning, as it can improve the understanding of complex reaction mechanisms.
Key Takeaways
- Introduces a two-dimensional RMSD projection method for reaction path visualization.
- Enhances the ability to analyze optimization trajectories beyond traditional methods.
- Demonstrates applicability through various chemical reactions, including cycloaddition and rearrangements.
Physics > Chemical Physics arXiv:2512.07329 (physics) [Submitted on 8 Dec 2025 (v1), last revised 17 Feb 2026 (this version, v3)] Title:Two-dimensional RMSD projections for reaction path visualization and validation Authors:Rohit Goswami (1) ((1) Institute IMX and Lab-COSMO, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland) View a PDF of the paper titled Two-dimensional RMSD projections for reaction path visualization and validation, by Rohit Goswami (1) ((1) Institute IMX and Lab-COSMO and 3 other authors View PDF HTML (experimental) Abstract: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 are often history dependent. This precludes the ability to compare optimization histories of different methods beyond the number of calculations, time taken, and final saddle geometry. We present a method mapping trajectories onto a two-dimensional projection 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 a gradient aware derivative Gaussian Process. This repr...
