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Updated: Jun 28, 2025

Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys
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一种积极的机器学习方法,用于优化合金的设计,使用贝叶斯优化

M Ghorbani1,2, M Boley3, P N H Nakashima4

  • 1Department of Materials Science and Engineering, Monash University, Melbourne, VIC, 3800, Australia. marzie.ghorbani@deakin.edu.au.

Scientific reports
|April 9, 2024
PubMed
概括

本研究介绍了贝叶斯优化工作流程,用于设计具有所需机械性能的 (Mg) 合金. 该方法使用主动学习和高斯过程回归来有效地识别最佳合金组成.

关键词:
合金设计 合金设计贝叶斯优化的贝叶斯优化机器学习 机器学习Mg 合金 Mg 合金顺序设计策略的顺序设计策略.

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