Reproducible Orchestration of Best Practices for Reaction Path Optimization with the Nudged Elastic Band

Abstract

The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-processing steps: endpoint minimization, structural alignment, and initial path generation. These steps are typically handled by ad-hoc scripts or manual intervention, introducing errors and hindering reproducibility. We present a fully automated, open-source Snakemake workflow for small gas phase molecules that couples modern machine learning potentials (PET-MAD) to the eOn saddle point search software. Each step of the calculation lifecycle is encoded as an explicit dependency graph, from model retrieval and endpoint preparation through path initialization and band optimization. The workflow resolves all software dependencies from conda-forge, ensuring identical execution across platforms. Validation on the HCN to HNC isomerization demonstrates that the automated pipeline recovers the known single-barrier energy profile and product energy without manual intervention.

Figures (6)

• Figure 1: Workflow pipeline showing the Snakemake dependency graph. Raw endpoints undergo minimization and IRA alignment before SIDPP path generation. The hybrid CI-NEB-MMF optimization then refines the path to the MEP and transition state.
• Figure 2: Hybrid CI-NEB-MMF optimization strategy. The CI-NEB phase performs global path optimization with a climbing image. When the climbing image force falls below threshold ($\approx 0.5$ eV/Å), the method switches to MMF for local saddle refinement via the lowest curvature mode.
• Figure 3: Two-stage IRA alignment process. Raw endpoints are centered and aligned before minimization to establish consistent atom ordering. After geometry relaxation, alignment is reapplied to correct any atom mapping drift introduced during optimization. Both stages solve the joint rotation-permutation problem via the IRA algorithm.
• Figure 4: Sequential IDPP (SIDPP) path growth. Unlike standard IDPP which interpolates all images simultaneously, SIDPP adds one image at a time, alternating between reactant and product sides. After each addition, all intermediate images are re-optimized. The step size parameter $\alpha$ controls placement of each new image relative to the frontier. This sequential growth avoids local minima that trap simultaneous interpolation for complex reactions.
• Figure 5: One-dimensional energy profile for the HCN $\to$ HNC isomerization. The single barrier of 2.46 eV separates the HCN reactant from the HNC product (0.57 eV above HCN). Colored traces show the optimization history from initial SIDPP guess (outer traces) to the converged path (black). Inset structures show the reactant, saddle point, and product geometries.