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基于机器学习和多目标优化,用于高合金的加速设计.

Yingying Ma1, Minjie Li2, Yongkun Mu3

  • 1Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, China.

Journal of chemical information and modeling
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PubMed
概括
此摘要是机器生成的。

机器学习加速了耐磨性高合金 (HEA) 的设计. 这项研究开发了一个框架来预测和优化HEA硬度和可塑性,确定有希望的新合金组合物.

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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
  • 合金设计设计 合金设计

背景情况:

  • 高合金 (HEAs) 由于其高硬度和柔性,为耐磨应用提供了潜力.
  • 传统的合金设计方法与HEAs的庞大组成空间作斗争,阻碍了发现具有多个理想性质的新材料.

研究的目的:

  • 开发基于机器学习 (ML) 的框架,用于设计具有增强维克尔硬度 (H) 和压力裂变应变 (D) 的高合金 (HEA).
  • 通过虚拟选和遗传算法识别最佳合金组合物.

主要方法:

  • 构建一个大型数据集 (172,467个数据点),其中有161个用于预测H和D的特征.
  • 使用支持向量回归 (SVR) 和光梯度增强机 (LightGBM) 算法进行特征选择,确定D的12个特征和H的8个特征.
  • 应用非主导排序基因算法II (NSGA-II) 和虚拟选来发现最佳的HEA组合.

主要成果:

  • 机器学习模型在10倍交叉验证过程中实现了高预测准确性,R平方值为D的0.76和H的0.90.
  • 合成和验证了四种新的HEA候选物,其中三种在可比硬度水平下显著改善了柔性 (135.8%,282.4%,194.1%).
  • 对Al,Nb和Mo的推元素组成范围,以在HEAs中实现高硬度和可塑性.

结论:

  • 拟议的ML框架有效地加速了设计和发现具有优越硬度和可塑性的高合金.
  • 经过验证的HEA候选物和推的成分范围为开发先进耐磨材料提供了宝贵的指导.