Random forest incorporating ab-initio calculations for corrosion rate prediction with small sample Al alloys data
Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large...
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| Published in | Npj Materials degradation Vol. 6; no. 1; pp. 1 - 10 |
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| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
07.10.2022
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we propose a strategy Integrating Ab-initio Calculations with Random Forest (IACRF) to optimize the model, thereby estimating the corrosion rate of Al alloys in diverse environments. Based on the thermodynamic assessment of the secondary phases, the ab-initio calculation quantities, especially the work function, significantly improved the prediction accuracy with respect to small-sample Al alloys corrosion dataset. To build a better generic prediction model, the most accessible and effective features are identified to train IACRF. Finally, the independent field exposure experiments in Southeast Asia have proven the generalization ability of IACRF in which the average prediction accuracy is improved up to 91%. |
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| AbstractList | Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we propose a strategy Integrating Ab-initio Calculations with Random Forest (IACRF) to optimize the model, thereby estimating the corrosion rate of Al alloys in diverse environments. Based on the thermodynamic assessment of the secondary phases, the ab-initio calculation quantities, especially the work function, significantly improved the prediction accuracy with respect to small-sample Al alloys corrosion dataset. To build a better generic prediction model, the most accessible and effective features are identified to train IACRF. Finally, the independent field exposure experiments in Southeast Asia have proven the generalization ability of IACRF in which the average prediction accuracy is improved up to 91%. Abstract Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we propose a strategy Integrating Ab-initio Calculations with Random Forest (IACRF) to optimize the model, thereby estimating the corrosion rate of Al alloys in diverse environments. Based on the thermodynamic assessment of the secondary phases, the ab-initio calculation quantities, especially the work function, significantly improved the prediction accuracy with respect to small-sample Al alloys corrosion dataset. To build a better generic prediction model, the most accessible and effective features are identified to train IACRF. Finally, the independent field exposure experiments in Southeast Asia have proven the generalization ability of IACRF in which the average prediction accuracy is improved up to 91%. |
| ArticleNumber | 83 |
| Author | Li, Ni Li, Xiaogang Dey, Poulumi Cheng, Zhanming Ao, Min Li, Menglin Sun, Xiaoguang Xiao, Kui Ji, Yucheng Fu, Xiaoqian Zhang, Dawei Chowwanonthapunya, Thee Dong, Chaofang Ren, Jingli |
| Author_xml | – sequence: 1 givenname: Yucheng surname: Ji fullname: Ji, Yucheng organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Department of Materials Science and Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Shunde Innovation School, University of Science and Technology Beijing – sequence: 2 givenname: Ni surname: Li fullname: Li, Ni organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Shunde Innovation School, University of Science and Technology Beijing – sequence: 3 givenname: Zhanming surname: Cheng fullname: Cheng, Zhanming organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Shunde Innovation School, University of Science and Technology Beijing – sequence: 4 givenname: Xiaoqian surname: Fu fullname: Fu, Xiaoqian organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Shunde Innovation School, University of Science and Technology Beijing – sequence: 5 givenname: Min surname: Ao fullname: Ao, Min organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Shunde Innovation School, University of Science and Technology Beijing – sequence: 6 givenname: Menglin surname: Li fullname: Li, Menglin organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Shunde Innovation School, University of Science and Technology Beijing – sequence: 7 givenname: Xiaoguang orcidid: 0000-0002-0463-5519 surname: Sun fullname: Sun, Xiaoguang organization: Technical Engineering Department, CRRC Qingdao Sifang Co. Ltd – sequence: 8 givenname: Thee surname: Chowwanonthapunya fullname: Chowwanonthapunya, Thee organization: Faculty of International Maritime Studies, Kasetsart University – sequence: 9 givenname: Dawei orcidid: 0000-0002-6546-6181 surname: Zhang fullname: Zhang, Dawei organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Shunde Innovation School, University of Science and Technology Beijing – sequence: 10 givenname: Kui orcidid: 0000-0002-1172-195X surname: Xiao fullname: Xiao, Kui organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing – sequence: 11 givenname: Jingli orcidid: 0000-0002-5392-291X surname: Ren fullname: Ren, Jingli organization: Henan Academy of Big Data, Zhengzhou University – sequence: 12 givenname: Poulumi surname: Dey fullname: Dey, Poulumi organization: Department of Materials Science and Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology – sequence: 13 givenname: Xiaogang surname: Li fullname: Li, Xiaogang organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Shunde Innovation School, University of Science and Technology Beijing – sequence: 14 givenname: Chaofang surname: Dong fullname: Dong, Chaofang email: cfdong@ustb.edu.cn organization: Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Shunde Innovation School, University of Science and Technology Beijing |
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| SubjectTerms | 639/301/1023/1026 639/301/1034/1037 639/705 Accuracy Aluminum base alloys Big Data Chemistry and Materials Science Corrosion Corrosion and Coatings Corrosion rate Datasets Electrochemistry Engineering Experiments Intermetallic compounds Machine learning Materials Science Materials selection Prediction models Sensors Structural Materials Tribology Work functions |
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| Title | Random forest incorporating ab-initio calculations for corrosion rate prediction with small sample Al alloys data |
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