Cloudra framework improves automated exploration of chemical reactions
Cloudra: Geometric Reasoning and Scientific Validation for Autonomous Reaction Mechanism Exploration
Multiagent Systems
Summary
Understanding how chemicals react involves figuring out which steps happen, how molecules look in 3D, and what leads to what. The authors found that using large language models alone can cause mistakes in understanding these steps. They created Cloudra, a system that uses multiple agents and chemical data to organize and check reaction steps carefully. Cloudra was tested on many reaction tasks and showed better accuracy and consistency than simpler approaches.
What this means in practice
- •For computational chemists: Automate and validate complex chemical reaction pathway exploration to improve reaction mechanism models.
- •For chemical simulation engineers: Integrate multi-agent checking and geometric validation to reduce errors in automated reactive system simulations.
Authors
Chengyi Yang, Qianxin Zhou, Yang Li
Abstract
Autonomous reaction-mechanism exploration requires proposing reaction steps, preparing three-dimensional structures, and determining which reactant and product a computed saddle point connects. Large language model (LLM) agents can misjudge spatial relationships and confuse a computed structure with the species or step they intended to study. We present Cloudra, a multi-agent framework that constrains mechanistic output through a Delivery DAG of chemical objects. Species nodes carry geometries and bonding topology; elementary-step edges carry transition states and reaction connections between species. Cheminformatics algorithms establish molecular identities and atom correspondences. Deterministic routines measure and transform structures to prepare candidate geometries. Frequency and IRC calculations determine the stationary points found and the species they connect. Agents use these objects to test proposed steps, compare pathways, and revise the mechanism. CloudraBench evaluates the framework's end-to-end mechanism-calculation capability across 50 tasks covering elementary reactions, multistep mechanisms, stereochemical competition, and computational method limitations. Across 200 runs under four workflow configurations, the primary Guided workflow achieves a mean composite score of 0.805, compared with 0.584 for the same-model Standard baseline. On Hard-tier tasks, the Guided mean increases from 0.689 under Standard to 0.958. Under Guided, the self-consistency component reaches its maximum score in 24 of the 40 non-Impossible tasks, compared with seven under Standard. Behavioral analyses examine how the agents use bond and dihedral measurements, optimized structures, and IRC endpoints to revise local mechanistic assignments.