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.
@article{arxiv.2312.15899,
title = {Corrosion-resistant aluminum alloy design through machine learning combined with high-throughput calculations},
author = {Yucheng Ji and Xiaoqian Fu and Feng Ding and Yongtao Xu and Yang He and Min Ao and Fulai Xiao and Dihao Chen and Poulumi Dey and Kui Xiao and Jingli Ren and Xiaogang Li and Chaofang Dong},
journal= {arXiv preprint arXiv:2312.15899},
year = {2023}
}