微结构的快速反向设计通过生成的不变性网络
Xian Yeow Lee1, Joshua R Waite1, Chih-Hsuan Yang1
1Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
Nature computational science
|January 6, 2024
概括
本研究引入了一种使用生成对抗网络 (GAN) 快速设计具有光伏应用特定特性的材料微结构的新方法,加速材料发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算工程 计算工程
- 太阳能光伏发电是如何实现的
背景情况:
- 具有目标性质的材料微结构的高效设计在许多工程领域都至关重要.
- 传统的微观结构敏感设计往往是代性的和耗时的.
- 快速生成具有用户指定的属性的微结构可以显著提高设计工作流程.
研究的目的:
- 使用受约束的生成对抗网络 (GAN) 模型重新制定微结构设计.
- 开发一种用于快速生成具有用户定义性能特征的光伏应用的两相形态的方法.
- 将基于物理的约束纳入数据驱动的反向设计框架.
主要方法:
- 利用一个受约束的生成对抗网络 (GAN) 模型来编码不变性约束.
- 开发了基于深度学习的可微分替代物,用于物理模型将微结构映射到光伏属性.
- 提出了一种多忠度替代方法,以将数据标签成本降低五倍.
主要成果:
- 成功生成了具有用户定义的短路电流密度和填充因子组合的微结构.
- 在短短190毫秒的时间内实现了快速的微结构生成.
- 证明了将复杂,不可区分的约束纳入设计过程的能力.
结论:
- 拟议的物理意识,数据驱动的框架加速了具有所需性质的微观结构的反向设计.
- 这种方法改变了微结构敏感设计的代性质.
- 能够更快地开发用于光伏和其他应用的先进材料.
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