Incorporation of Internal Coordinates Interpolation into the Freezing String Method

TL;DR

This paper introduces an enhanced freezing string method that incorporates internal coordinates interpolation, significantly improving the efficiency and success rate of transition state searches in chemical reactions.

Contribution

The authors develop and validate an improved FSM that uses internal coordinates interpolation, enabling larger steps and fewer optimizations, reducing computational costs.

Findings

Nearly 50% reduction in computational cost.

100% success rate on benchmark reactions.

Effective even where previous methods failed.

Abstract

We present an improved method for determining guess structures for transition state searches by incorporating internal coordinates interpolation into the freezing string method (FSM). We test our method on over 40 reactions across 3 benchmark datasets covering a diverse set of chemical reactions. Our results show that incorporation of internal coordinates interpolation improves the reliability of the FSM, enabling larger interpolation step sizes and fewer optimization steps per cycle, which together yield nearly a 50\% reduction in computational cost while maintaining a 100\% success rate on benchmark chemical reaction test cases, including systems where previous attempts based on linear synchronous transit interpolation have failed. We provide an open-source Python implementation of the FSM, in addition to the reactant, product, and transition state structures of all reactions studied.