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

摘要

Efficiently designing lightweight alloys with combined high corrosion resistance and strength remains an enduring topic in materials engineering. To this end, machine learning (ML) coupled ab-initio calculations is proposed within this study. Due to the inadequate accuracy of conventional stress -strain ML models caused by corrosion environment factors, a novel reinforcement self -learning ML algorithm (accuracy R 2 > 0.92) is developed. Then, a strat egy that integrates ML models, calculated energetics, and mechanical properties is implemented to optimize the Al alloys. Next, this Computation Designed Corrosion-Resistant Al alloy is fabricated that verified the simulation. The performance (elongation reaches ~30%) is attributed to the H-captured Al -Sc-Cu phases and Cu -modified η/η' precipitation inside the grain boundaries (GBs). The trained Al -Mg-Zn-Cu interatomic potential (energy accuracy 6.50 meV/atom) proves the cracking resistance of GB region enh anced by Cu - modification. Conceptually, our strategy is of practical importance for designing new alloys exhibiting combined exceptional properties.