Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy
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Abstract
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 R2 >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−1) and Cu-modified η/η' precipitates inside the grain boundaries (GBs).
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File under embargo until 16-10-2024