使用机器学习对高合金和无形金属合金进行预测建模
Son Gyo Jung1,2,3, Guwon Jung1,3,4, Jacqueline M Cole1,2,3
1Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
Journal of chemical information and modeling
|October 1, 2024
概括
机器学习加速了先进合金的发现,如高合金和无形金属合金. 这种数据驱动的方法有效地预测材料的特性,克服了构成多样性的挑战.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算材料科学科学 计算材料科学
- 在材料发现领域的机器学习
背景情况:
- 高合金 (HEAs) 和无形金属合金 (AMA) 是具有特殊特性,特别是高强度的先进材料.
- 它们庞大的组成空间为传统的实验和计算探索带来了重大挑战.
- 现有的数据驱动方法受到数据稀缺和缺乏强大的预测工具的限制,这些工具将组成与属性联系起来.
研究的目的:
- 开发和验证机器学习 (ML) 工作流程,以加快HEA和AMA的数据驱动发现和优化.
- 为应对这些高级合金类别广泛的组成多样性所带来的挑战.
- 为关键材料属性创建预测模型,增强系统的探索.
主要方法:
- 实施了基于机器学习的工作流程,包括功能选择和统计分析.
- 利用贝叶斯优化来开发一个回归模型来预测散装模量.
- 开发了一种分类模型来预测玻璃形成能力,利用文献中的各种化学数据.
主要成果:
- 预测散装模块的回归模型实现了0.969的R平方,MAE为3.958 GPa,RMSE为5.411 GPa.
- 玻璃成形能力的分类模型实现了F1得分为0.91,AUC为0.98,准确度为0.91.
- 通过整合广泛的化学数据,成功预测了广泛的特性,证明了该模型的有效性.
结论:
- 开发的ML工作流有效地加速了先进合金材料的发现和优化.
- 特性分析和选择对于构建强大的预测模型至关重要,其性能优于单纯依赖复杂技术.
- 这种方法为导航HEA和AMA复杂的组成格局提供了一个强大的工具.
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