Autonomous Chemical Mechanistic Discovery through Agentic Reasoning and Validation

2026-09-10

AI Summary

本文介绍了一种名为ARCHE的自主系统，通过整合通用推理模型、计算化学模型和工具注册表来自动发现化学反应机制，并在三个不同场景中验证了其有效性。

Abstract

Unraveling reaction mechanisms is central to modern chemistry, yet automating these investigations remains challenging because computational workflows still rely heavily on expert intervention. Here we introduce ARCHE, an autonomous agentic system that integrates a general-purpose reasoning model, a domain-specialized computational chemistry model, and a structured tool registry to transform mechanistic inquiry into a scalable, self-validating process. ARCHE interprets scientific questions, generates and prioritizes mechanistic hypotheses, orchestrates computational workflows, and iteratively refines conclusions based on computed evidence within a closed loop. We validate its capabilities across three increasingly demanding scenarios: reconstructing stereocontrolling transition states and validating the corresponding reaction mechanism in a previously reported asymmetric catalytic reaction; proposing and validating a plausible radical pathway through iterative hypothesis refinement for a recently discovered but unpublished $α$-iodoboronate C-I cleavage reaction; and identifying a chemically interpretable descriptor that governs selectivity in nickel-catalysed migratory cross-coupling reactions. By coupling agentic reasoning with rigorous computational validation, ARCHE advances autonomous mechanistic discovery and establishes a foundation for broader machine-assisted chemical research. The code for ARCHE is publicly available at https://github.com/JetAstra/Arche-Harness.

Problem

• Autonomous Chemical Mechanistic Discovery
• Agentic Reasoning
• Computational Chemistry
• Expert Intervention
• Mechanistic Hypotheses

Innovation

• Autonomous Agentic System
• Mechanistic Discovery
• Computational Validation
• Hypothesis Refinement