# Adaptive Transition-State Refinement with Learned Equilibrium Flows

## TL;DR

This paper introduces a new AI method that improves the accuracy and efficiency of finding transition states in chemical reactions.

## Contribution

A novel generative AI approach that refines initial guesses for transition-state structures, improving accuracy and success rates.

## Key findings

- The method reduces structural error to 0.077 Å when applied to machine-learning model guesses.
- It increases success rates by 41% when starting from tight-binding approximations.
- High-level quantum optimization is sped up by a factor of 3 using this approach.

## Abstract

Identifying transition
states (TSs), the high-energy configurations
that molecules pass through during chemical reactions, is essential
for understanding and designing chemical processes. However, accurately
and efficiently identifying these states remains one of the most challenging
problems in computational chemistry. In this work, we introduce a
new generative AI approach that improves the quality of initial guesses
for TS structures. Our method can be combined with a variety of existing
techniques, including both machine-learning models and fast, approximate
quantum methods, to refine their predictions and bring them closer
to chemically accurate results. Applied to TS guesses from a state-of-the-art
machine-learning model, our approach reduces the median structural
error to 0.077 Å and lowers the median absolute error in reaction
barrier heights to 0.40 kcal mol–1. When starting
from a widely used tight-binding approximation, it increases the success
rate of locating valid TSs by 41% and speeds up high-level quantum
optimization by a factor of 3. By making TS searches more accurate,
robust, and efficient, this method could accelerate reaction mechanism
discovery and support the development of new materials, catalysts,
and pharmaceuticals.