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

TL;DR

This paper introduces an automated, reproducible workflow for reaction path optimization using the nudged elastic band method, integrating machine learning potentials and ensuring cross-platform consistency.

Contribution

It presents a fully automated, open-source Snakemake workflow that couples modern ML potentials with NEB calculations, improving reproducibility and reducing manual errors.

Findings

Successfully recovers known energy profiles for HCN to HNC isomerization.

Automates all steps from model retrieval to path optimization, ensuring reproducibility.

Resolves software dependencies for consistent results across platforms.

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.