  Efficiently designing lightweight alloys with combined high corrosion
resistance and mechanical properties 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 factors, a novel reinforcement
self-learning ML algorithm (accuracy R2 >0.92) is developed. Then, a strategy
that integrates ML models, calculated energetics and mechanical moduli 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 (-1.44 eV H-1) and Cu-modified {\eta}/{\eta}' precipitation inside the
grain boundaries (GBs). The developed Al-Mg-Zn-Cu interatomic potential (energy
accuracy 6.50 meV atom-1) proves the cracking resistance of the GB region
enhanced by Cu-modification. Conceptually, our strategy is of practical
importance for designing new alloys exhibiting corrosion resistance and
mechanical properties.
