Generative artificial intelligence for reliable mechanistic reasoning for corrosion

AI can explain its reasoning in safety-critical fields: Unlike standard machine learning models that only predict corrosion rates, this system generates mechanistic explanations grounded in scientific literature, addressing a key limitation in materials engineering where understanding why something happens is as important as predicting that it will happen.

Expert verification during training improves reliability: The models were fine-tuned on 3,309 question-answer pairs verified by domain experts from 840 peer-reviewed papers, achieving 96.4% system faithfulness and 98.8% context recall—demonstrating that curated, expert-validated training data significantly enhances trustworthiness in specialized domains.

New evaluation methods catch errors that standard metrics miss: The "Reason Map" framework detects specific types of reasoning failures (like reversed causal relationships and unsupported logical leaps) that conventional factuality metrics overlook, providing a more rigorous way to validate AI reasoning in technical fields.

Real-world validation matters: The system was tested on newly published literature and actual laboratory electrochemical data, confirming it generalizes beyond its training set—an important step often missing in AI research that demonstrates practical applicability rather than just benchmark performance.

Modular design enables broader application: The architecture can be adapted across different technical domains beyond corrosion, suggesting a replicable pathway for deploying trustworthy AI systems in other safety-critical fields like aerospace, medicine, or infrastructure.