Title
Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy
Author
Ji, Y. (TU Delft Materials Science and Engineering; University of Science and Technology Beijing)
Fu, Xiaoqian (University of Science and Technology Beijing)
Ding, Feng (University of Science and Technology Beijing)
Xu, Yongtao (General Research Institute for Nonferrous Metals)
He, Yang (University of Science and Technology Beijing)
Ao, Min (University of Science and Technology Beijing)
Xiao, Fulai (Shandong Nanshan Aluminum Co.)
Chen, Dihao (University of Science and Technology Beijing)
Dey, P. (TU Delft Team Poulumi Dey) 
Qin, Wentao (University of Science and Technology Beijing)
Xiao, Kui (University of Science and Technology Beijing)
Ren, Jingli (Zhengzhou University)
Kong, Decheng (Shanghai Jiao Tong University)
Li, Xiaogang (University of Science and Technology Beijing)
Dong, Chaofang (University of Science and Technology Beijing)
Department
Materials Science and Engineering
Date
2024
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).
Subject
Al-Zn-Mg alloys
First-principles calculation
Machine learning
Molecular dynamic simulation
Precipitates
To reference this document use:
http://resolver.tudelft.nl/uuid:e02260c6-2134-4ac5-b43a-614e394eff1f
DOI
https://doi.org/10.1016/j.corsci.2024.112062
Embargo date
2024-10-16
ISSN
0010-938X
Source
Corrosion Science: the journal on environmental degradation of materials and its control, 233
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 Y. Ji, Xiaoqian Fu, Feng Ding, Yongtao Xu, Yang He, Min Ao, Fulai Xiao, Dihao Chen, P. Dey, Wentao Qin, Kui Xiao, Jingli Ren, Decheng Kong, Xiaogang Li, Chaofang Dong