Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy
Yucheng Ji (TU Delft - Materials Science and Engineering, University of Science and Technology Beijing)
Xiaoqian Fu (University of Science and Technology Beijing)
Feng Ding (University of Science and Technology Beijing)
Yongtao Xu (General Research Institute for Nonferrous Metals)
Yang He (University of Science and Technology Beijing)
Min Ao (University of Science and Technology Beijing)
Fulai Xiao (Shandong Nanshan Aluminum Co.)
Dihao Chen (University of Science and Technology Beijing)
Poulumi Dey (TU Delft - Team Poulumi Dey)
Wentao Qin (University of Science and Technology Beijing)
Kui Xiao (University of Science and Technology Beijing)
Jingli Ren (Zhengzhou University)
Decheng Kong (Shanghai Jiao Tong University)
Xiaogang Li (University of Science and Technology Beijing)
Chaofang Dong (University of Science and Technology Beijing)
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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).