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超级合金的数据驱动疲劳预测:一种新的战略,集成转移学习和部分标签学习,以解决模两可的数据
Haopeng Lv1, Jiawei Yin1, Dayong Wu1,2
1School of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, 050018, P. R. China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 7, 2025
概括
本研究引入了一种新的机器学习框架,用于预测超合金疲劳性能,有效处理模两可的数据. 该方法通过将组成,微观结构和属性联系起来来提高准确性和可解释性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 机器学习应用 机器学习应用
背景情况:
- 机器学习 (ML) 提供了高效的材料属性预测,但与模两可的数据作斗争.
- 数据完整性问题,特别是模两可的数据,阻碍了ML在材料科学中的广泛应用.
- 预测超级合金的疲劳性能对于工程应用至关重要.
研究的目的:
- 开发一种新的ML策略,以使用模两可的组成数据预测超合金疲劳性能.
- 通过揭示组成-微观结构-属性关系来提高模型的解释性.
- 建立适用于更广泛数据集的材料性质预测的强大框架.
主要方法:
- 整合部分标签学习和转移学习来管理模两可的组成数据.
- 通过基于构成的热力学计算来丰富微观结构特征.
- 在超级合金中开发ML模型来预测疲劳性能.
主要成果:
- 实现了超级合金疲劳性能的卓越预测准确度.
- 通过实验数据验证的强大的概括能力.
- 通过揭示底层组成-微观结构-属性相关性,提高了模型的解释性.
结论:
- 拟议的框架有效地解决了材料属性预测中的模两可的数据挑战.
- 部分标签和转移学习的整合为材料信息学提供了一个强大的方法.
- 这种方法在推进材料设计和发现方面具有重大潜力.
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