Evolving transition-state search with agentic large language models
TL;DR
An adaptive evolutionary framework for TS search workflows that prioritizes completion of a target dataset over global algorithmic robustness and suggests that adaptive, LLM-driven evolutionary workflow optimization provides a transferable strategy for improving validated TS coverage in large-scale, failure-prone scientific workflows.
Abstract
Accurate identification of transition states (TSs) is fundamental to computational chemistry. Modern reaction-discovery efforts increasingly rely on curating and completing large reaction datasets, where even a small fraction of TS-search failures can leave key pathways unresolved and bias the resulting reaction network. Here, we introduce an adaptive evolutionary framework for TS search workflows that prioritizes completion of a target dataset over global algorithmic robustness. The framework iteratively focuses on reactions for which no validated TS has been found and dynamically modifies the workflow to search the remaining TSs. Rather than converging toward a single universally optimal TS search workflow, the framework generates a sequence of specialized workflow variants that collectively increase TS coverage across the dataset. Applied to the Transition1X benchmark (over 10,000 reactions), this adaptive evolution improves a state-of-the-art TS-search workflow to achieve a >80% success rate of TS-finding with rigorous validation via eigenvector analysis and reaction path endpoint confirmation. More broadly, these results suggest that adaptive, LLM-driven evolutionary workflow optimization provides a transferable strategy for improving validated TS coverage in large-scale, failure-prone scientific workflows.
