Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure
Guangxuan Song, Dongmei Fu, Yongjie Lin, Lingwei Ma, Dawei Zhang
Abstract
The prediction of corrosion resistance in High-entropy alloys (HEAs) faces challenges due to previous machine learning methods not fully capturing the interdependencies between composition, processing, and crystal structure. This study proposes the Composition and Processing-Driven Two-Stage Corrosion Prediction Framework with Structural Prediction (CPSP Framework), which first predicts crystal structure and then combines composition and processing data for corrosion current prediction. A deep learning model, Mat-NRKG, is developed based on the CPSP framework, efficiently integrating composition, processing, and crystal structure data through a knowledge graph and graph convolutional network. Evaluations using the HEA-CRD dataset show that the CPSP Framework outperforms the Composition-Only Prediction Framework (CP Framework) and the Composition and Processing-Based Prediction Framework (CPP Framework). The Mat-NRKG model demonstrates the best performance on the HEA-CRD dataset. Its generalization capability is validated through experiments on five laboratory-synthesized HEAs, highlighting the effectiveness of incorporating prior knowledge into model design for performance prediction.