Locating Ab Initio Transition States via Approximate Geodesics on Machine Learned Potential Energy Surfaces

TL;DR

This paper introduces a novel method for locating transition states in computational chemistry by constructing geodesic paths on machine-learned potential energy surfaces, significantly reducing computational effort and eliminating the need for ab initio calculations.

Contribution

The authors develop an algorithm to construct geodesic paths on ML-generated PES for transition state guesses, improving efficiency over traditional methods.

Findings

Geodesic-based guesses reduce optimization steps by 30% on average.

The approach eliminates the need for ab initio calculations in transition state guessing.

Effective for complex chemical reaction networks.

Abstract

Efficient and reliable identification and optimization of transition state structures is a longstanding challenge in computational chemistry. Popular chain-of-states methods require hundreds if not thousands of ab initio calculations to generate initial guesses for local quasi-Newton optimizers, with persistent risk of collapse to an alternative stationary point on the potential energy surface (PES). Here, we show that high-quality guess structures for transition state optimization can be obtained by constructing the geodesic path between reactant and product structures on the PES generated by machine learning potentials (MLPs). We present an algorithm for optimization of such geodesic paths, as well as the associated codebase. We demonstrate effectiveness of this approach using the recent eSEN-sm-cons MLP. On average, the highest-energy point along these MLP geodesics requires 30% fewer quasi-Newton optimization steps to converge to the transition state compared to guesses from the fully ab initio frozen string method. Our approach therefore completely eliminates the need for ab initio calculations for generation of transition state guesses and considerably speeds up subsequent structural optimization. Geodesic construction on ML PES thus promises to be a useful approach for efficient computational elucidation of complex chemical reaction networks.