用于可靠腐蚀机理解释的生成式人工智能

中文摘要 AI 辅助

腐蚀约占全球GDP的4%，可靠预测对及时防控至关重要。机器学习可根据成分、微观结构和环境变量有效预测腐蚀速率，但无法解释潜在机制。安全关键材料工程中的可靠方法不仅需要准确检索，还需具备机理上可辩护的推理能力，这是现有事实性指标无法评估的。本研究提出一种针对腐蚀知识合成的领域适配检索增强生成框架，以镁合金腐蚀为案例进行验证。对Llama-3.1-8B、Qwen-2.5-7B、Mistral-7B三款开源权重语言模型，基于840篇同行评审论文中的3309个经专家验证的问答对进行微调，并与混合密集-词汇检索管道集成。检索增强使Token F1提升143%-194%，系统忠实度为0.964，上下文召回率为0.988。对新发表文献及内部电化学数据的盲法外部验证证实了趋势层面的泛化能力。研究进一步引入命题图框架Reason Map，其可独立从生成答案和检索文献构建有向证据图，能系统检测因果方向反转及无支撑的推理跳跃，而这些是平面事实性指标无法暴露的。该模块化架构可跨领域应用，为规避腐蚀的可信人工智能辅助知识合成提供可泛化蓝图，也可应用于其他工程领域。

英文摘要

Corrosion accounts for approximately 4% of global GDP, and reliable prediction is essential for timely mitigation. Machine learning effectively predicts corrosion rates from composition, microstructure, and environmental variables, but cannot explain the underlying mechanisms. A reliable approach in safety-critical materials engineering requires not only accurate retrieval but also mechanistically defensible reasoning, a capability that existing factuality metrics cannot assess. This work presents a domain-adapted retrieval-augmented generation framework for corrosion knowledge synthesis, demonstrated on magnesium alloy corrosion. Three open-weight language models (Llama-3.1-8B, Qwen-2.5-7B, Mistral-7B) are fine-tuned on 3,309 expert-verified question-answer pairs from 840 peer-reviewed papers and integrated with a hybrid dense-lexical retrieval pipeline. Retrieval augmentation produces Token F1 gains of 143-194%, with system faithfulness of 0.964 and context recall of 0.988. Blind external validation on newly published literature and in-house electrochemical data confirms trend-level generalisation. Reason Map, a proposition-graph framework, is further introduced; it independently constructs directed evidence graphs from generated answers and retrieved literature, enabling systematic detection of causal direction inversions and unsupported inferential leaps that flat factuality metrics cannot expose. The modular architecture can be applied across domains, offering a generalizable blueprint for trustworthy AI-assisted knowledge synthesis to circumvent corrosion, which can also be applied to other engineering domains.

发表机构

• Indian Institute of Technology Bombay（印度理工学院孟买分校）
• Monash University（莫纳什大学）
• IITB-Monash Research Academy（印度理工学院孟买分校-莫纳什大学联合研究院）

机构由 AI 辅助整理，请以论文原文为准。