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

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 R-2 >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 similar to 30 %) is attributed to the H trapping Al-Sc-Cu phases (-1.44 eV H-1 ) and Cu-modified eta/eta' precipitates inside the grain boundaries (GBs).