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条件扩散模型用于从机械性质中反向预测过程参数和树突微结构

Arisa Ikeda¹, Ryo Higuchi², Tomohiro Yokozeki²

¹Graduate School of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan.

Scientific reports

|October 23, 2025

概括

本研究介绍了一种数据驱动的反向设计方法,使用条件扩散模型来预测材料开发中所需机械性能的最佳过程参数和微结构,从而降低实验成本.

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
聚合物科学聚合物科学

背景情况:

由于广泛的实验和模拟,材料开发是昂贵的.
数据驱动的反向设计方法为材料开发提供了具有成本效益的方法.
了解微观结构与属性关系对于实现所需的机械性能至关重要.

研究的目的:

开发一种条件扩散模型,用于材料的反向设计.
预测特定机械性质的最佳过程参数和微结构.
为了减少与材料开发相关的成本和时间.

主要方法:

一个条件扩散模型的开发.
该模型应用于聚合物材料,特别是碳纤维增强热塑性塑料的矩阵树脂.
使用反向分析,根据所需的机械性质预测过程参数和微结构.

主要成果:

训练的扩散模型成功地提出了处理温度,并预测了给定的模和波桑比率的微观结构.
该模型证明了其能够表示复杂的树突微观结构的能力.
该方法适用于各种材料,同时可以处理多个过程参数和机械性能.

结论:

条件扩散模型为材料开发提供了一种高效的数据驱动的反向设计方法.
这种方法显著减少了对广泛的实验试验和模拟的需求.
开发的模型提供了一种多功能工具,可以通过微观结构和过程参数预测来优化材料性能.

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