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Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design
Advanced Materials ( IF 27.4 ) Pub Date : 2023-06-18 , DOI: 10.1002/adma.202302530
Xiaoyang Zheng 1, 2 , Xubo Zhang 2 , Ta-Te Chen 3, 4 , Ikumu Watanabe 1, 2
Affiliation  

Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time-consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.

中文翻译:

机械超材料中的深度学习:从预测和生成到逆向设计

机械超材料是精心设计的结构,具有由其微观结构和组成材料决定的卓越机械性能。定制它们的材料和几何分布可以释放实现前所未有的整体特性和功能的潜力。然而,当前的机械超材料设计在很大程度上依赖于经验丰富的设计师通过反复试验获得的灵感,而研究其机械性能和响应则需要耗时的机械测试或计算量大的模拟。然而,深度学习的最新进展彻底改变了机械超材料的设计过程,无需先验知识即可实现属性预测和几何生成。此外,深度生成模型可以将传统的正向设计转变为逆向设计。最近许多关于在机械超材料中实施深度学习的研究都是高度专业化的,它们的优点和缺点可能不会立即显现出来。这篇批判性评论全面概述了深度学习在机械超材料的属性预测、几何生成和逆向设计方面的能力。此外,这篇评论还强调了利用深度学习创建普遍适用的数据集、智能设计的超材料和材料智能的潜力。这篇文章不仅对机械超材料研究人员有价值,而且对材料信息学领域的研究人员也有价值。
更新日期:2023-06-18
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