Constructing generalizable microstructure-property maps across diverse microstructure classes

Hao Liu

doi:10.1557/s43579-025-00873-z

2025-11-01

Hao Liu

Nirmal Baishnab

Balaji Sesha Sarath Pokuri

Baskar Ganapathysubramanian

Olga Wodo *

DOI：10.1557/s43579-025-00873-z

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摘要

En 中文

建立能够泛化到不同微观结构类别的微观结构-性能关联仍然是在数据驱动材料设计中的一项关键挑战。在本工作中，我们评估了将针对一种微观结构类型（例如，共扼分解结构）训练的预测模型外推到其他类型（例如，树枝状结构）的潜力，使用了三种不同的特征化策略：两点相关函数、基于图的描述符和深度神经网络嵌入。我们的发现表明，Wasserstein距离是一个优秀的指标，能够很好地与泛化性相关联，作为与模型无关但数据感知的泛化性特征。此外，我们证明了能够保持关键微观结构特征的特征化方法泛化性更好。

Keyword:

Microstructure

Machine Learning

期刊

MRS Communications

IF:

2.3

论文数:

244

被引数:

2.8K

机构

state university of new york (suny) system

学者数:

6.5W

论文数: 5.8W

被引数: 65

university at buffalo, suny

学者数:

1.2W

论文数: 9.5K

被引数: 9

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