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 >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) and Cu-modified η/η' precipitates inside the grain boundaries (GBs).

中文翻译：

---

人工智能结合高通量计算提高AlMgZn合金耐腐蚀性能

有效设计兼具高耐腐蚀性和机械性能的轻质合金仍然是材料工程领域的一个持久课题。针对腐蚀因素导致传统应力应变机器学习（ML）模型精度不足的问题，开发了一种结合计算特征的新型强化自学习ML算法（精度R>0.92）。基于 ML 模型、计算出的功函数和机械模量，制造并验证了计算设计的耐腐蚀铝合金。性能（伸长率达到~30%）归因于H捕获Al-Sc-Cu相（-1.44 eV H）和晶界（GB）内的Cu改性η/η'沉淀。