Machine-Learning-Based Prediction of Corrosion Behavior in Additively Manufactured Inconel 718
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Han Li & Ruotian Zhang & Yaosen Min & Dacheng Ma & Dan Zhao & Jianyang Zeng, 2023. "A knowledge-guided pre-training framework for improving molecular representation learning," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
- Tian Xie & Arthur France-Lanord & Yanming Wang & Jeffrey Lopez & Michael A. Stolberg & Megan Hill & Graham Michael Leverick & Rafael Gomez-Bombarelli & Jeremiah A. Johnson & Yang Shao-Horn & Jeffrey C, 2022. "Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
- Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
- Snehi Shrestha & Kieran James Barvenik & Tianle Chen & Haochen Yang & Yang Li & Meera Muthachi Kesavan & Joshua M. Little & Hayden C. Whitley & Zi Teng & Yaguang Luo & Eleonora Tubaldi & Po-Yen Chen, 2024. "Machine intelligence accelerated design of conductive MXene aerogels with programmable properties," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
- Oscar Méndez-Lucio & Christos A. Nicolaou & Berton Earnshaw, 2024. "MolE: a foundation model for molecular graphs using disentangled attention," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
- Xinyu Chen & Shuaihua Lu & Qian Chen & Qionghua Zhou & Jinlan Wang, 2024. "From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
- Zhiyuan Han & An Chen & Zejian Li & Mengtian Zhang & Zhilong Wang & Lixue Yang & Runhua Gao & Yeyang Jia & Guanjun Ji & Zhoujie Lao & Xiao Xiao & Kehao Tao & Jing Gao & Wei Lv & Tianshuai Wang & Jinji, 2024. "Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
- Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Sukriti Manna & Troy D. Loeffler & Rohit Batra & Suvo Banik & Henry Chan & Bilvin Varughese & Kiran Sasikumar & Michael Sternberg & Tom Peterka & Mathew J. Cherukara & Stephen K. Gray & Bobby G. Sumpt, 2022. "Learning in continuous action space for developing high dimensional potential energy models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Cong, Jian & Ma, Tianzeng & Chang, Zheshao & Zhang, Qiangqiang & Akhatov, Jasurjon S. & Fu, Mingkai & Li, Xin, 2023. "Neural network and experimental thermodynamics study of YCrO3-δ for efficient solar thermochemical hydrogen production," Renewable Energy, Elsevier, vol. 213(C), pages 1-10.
- Ribeiro, Haroldo V. & Lopes, Diego D. & Pessa, Arthur A.B. & Martins, Alvaro F. & da Cunha, Bruno R. & Gonçalves, Sebastián & Lenzi, Ervin K. & Hanley, Quentin S. & Perc, Matjaž, 2023. "Deep learning criminal networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
- Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
- Zeyin Yan & Dacong Wei & Xin Li & Lung Wa Chung, 2024. "Accelerating reliable multiscale quantum refinement of protein–drug systems enabled by machine learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Xinyu Chen & Yufeng Xie & Yaochen Sheng & Hongwei Tang & Zeming Wang & Yu Wang & Yin Wang & Fuyou Liao & Jingyi Ma & Xiaojiao Guo & Ling Tong & Hanqi Liu & Hao Liu & Tianxiang Wu & Jiaxin Cao & Sitong, 2021. "Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine learning," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
- Kangming Li & Daniel Persaud & Kamal Choudhary & Brian DeCost & Michael Greenwood & Jason Hattrick-Simpers, 2023. "Exploiting redundancy in large materials datasets for efficient machine learning with less data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
- Bin Xing & Timothy J. Rupert & Xiaoqing Pan & Penghui Cao, 2024. "Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
- Yi, Yong & Wang, Liming & Chen, Zhengying, 2021. "Adaptive global kernel interval SVR-based machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage," Renewable Energy, Elsevier, vol. 176(C), pages 81-88.
More about this item
Keywords
selective laser melting; Inconel 718; machine learning; corrosion prediction; extreme gradient boosting;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:6:y:2021:i:8:p:80-:d:601214. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.