Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy

Abstract

Efficiently designing lightweight alloys with combined high corrosion resistance and mechanical properties remains an enduring topic in materials engineering. Due to the inadequate accuracy of conventional stress-strain machine learning (ML) models caused by corrosion factors, a novel reinforcement self-learning ML algorithm combined with calculated features (accuracy R2 >0.92) is developed. Based on the ML models, calculated work functions and mechanical moduli, a Computation Designed Corrosion-Resistant Al alloy is fabricated and verified. The performance (elongation reaches ∼30%) is attributed to the H trapping Al-Sc-Cu phases (-1.44 eV H−1) and Cu-modified η/η' precipitates inside the grain boundaries (GBs).