Graph-based Learning and design of Advanced Mechanical Metamaterials

先进机械超材料的基于图形的学习和设计

• 批准号: EP/X02394X/1
• 负责人: Vikram Deshpande
• 金额: $ 274.53万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX02394X%2F1
• 关键词: Graph; based; Learning; design; Advanced

The emergence of additive manufacturing techniques has enabled the creation of complex 3D shapes with topological feature sizes spanning length scales from nanometres upwards. These manufacturing technologies have facilitated the creation of new materials (metamaterials) with previously unattainable properties such as light and recoverable ceramics. However, defects (deviations from the design) caused by manufacturing variabilities proliferate in topologically complex printed samples comprising millions of micro-scale elements. Traditional numerical simulations do not capture these a priori unknown defects, and thus the measured properties of fabricated metamaterials invariably deviate substantially from the designed/simulated properties. This low fidelity of the metamaterial simulation tools has left a vast portion of the metamaterial design space untapped. Leveraging recent foundational advances in machine learning and graph neural networks (GNNs), now is the ideal time for designing new additively manufactured materials with fully tailorable static and dynamic properties wherein, for example, a designer inputs a wave transmission spectrum from which a metamaterial that fully replicates the input response in an experimental setting is inversely designed and printed. Graph-based data-driven methods can address this challenge by their ability to learn from experimental data and efficiently encode the 3D material topology. The proposal will break new ground by exploiting breakthroughs in graph-based generative machine learning models to inversely generate metamaterials and thereby fuse the field of GNNs with mechanics, materials science, and additive manufacturing. This represents a fundamentally new realm of engineered material creation and discovery paradigm that will bridge the longstanding gap between simulation and experimental data of 3D printed metamaterials. The project will lay the scientific foundations for new engineering material designs and solutions.

增材制造技术的出现使得能够创建复杂的3D形状，其拓扑特征尺寸跨越纳米以上的长度尺度。这些制造技术促进了新材料（超材料）的创造，这些材料具有以前无法实现的特性，如轻质和可回收的陶瓷。然而，由制造可变性引起的缺陷（与设计的偏差）在包括数百万个微尺度元件的拓扑复杂的打印样品中激增。传统的数值模拟不捕捉这些先验未知的缺陷，因此，所制造的超材料的测量性能总是偏离设计/模拟的性能。超材料模拟工具的这种低保真度使得超材料设计空间的很大一部分未被开发。利用机器学习和图形神经网络（GNN）的最新基础进展，现在是设计具有完全可定制的静态和动态特性的新型增材制造材料的理想时机，其中，例如，设计师输入波透射谱，从中反向设计和打印完全复制实验设置中输入响应的超材料。基于图形的数据驱动方法可以通过从实验数据中学习并有效编码3D材料拓扑的能力来解决这一挑战。该提案将通过利用基于图形的生成机器学习模型的突破来反向生成超材料，从而将GNN领域与力学，材料科学和增材制造融合在一起。这代表了工程材料创造和发现范式的一个全新领域，将弥合3D打印超材料的模拟和实验数据之间的长期差距。该项目将为新的工程材料设计和解决方案奠定科学基础。

项目成果

Underexcitation prevents crystallization of granular assemblies subjected to high-frequency vibration.

• DOI: 10.1073/pnas.2306209120
• 发表时间: 2023-07-18
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Al Mahri, Sara;Grega, Ivan;Shaikeea, Angkur J. D.;Wadley, Haydn N. G.;Deshpande, Vikram S.
• 通讯作者: Deshpande, Vikram S.

Gravity enables self-assembly

• DOI: 10.1002/ntls.20220007
• 发表时间: 2022-07-01
• 期刊: NATURAL SCIENCES
• 作者: Grega, Ivan;Shaikeea, Angkur J. D.;Deshpande, Vikram S.
• 通讯作者: Deshpande, Vikram S.