Inverse Design and Mechanics of Hybrid Filler Composites with Solid and Liquid Inclusions

固体和液体夹杂物混合填料复合材料的逆向设计和力学

• 批准号: 2306613
• 负责人: Mohammad Malakooti
• 金额: $ 59.98万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306613&HistoricalAwards=false
• 关键词: Inverse; Design; Mechanics; Hybrid; Filler

Polymer composites with hybrid fillers are produced by a complex synthesis process involving two or more inclusions with different morpholoies and physical properties that often leads to laborious, time-consuming, low-yield, and expensive efforts. Furthermore, the uncertainty regarding their mechanical load capacity and susceptibility to premature failure hinders widespread adoption. This award supports fundamental research to inversely design composite materials with solid and liquid phase fillers while investigating their failure under large deformations. By building a comprehensive and interpretable machine learning model, this project will enable efficient and reliable synthesis of composites with targeted properties. The new knowledge will promote the utilization of soft multifunctional composites in emerging applications, such as self-powered wearable electronics, biomonitoring systems, and soft robotics. This award will also support the development of a diverse workforce through outreach programs and creation of free educational online content on topics of advanced mechanics and artificial intelligence.The objective of this research is to perform inverse design of “hybrid filler composites” with solid-liquid fillers and investigate their failure under large deformations. Embedded liquid-phase fillers add complexity to the mechanics of multiphase composites while offering unique advantages such as enhanced toughness and conductivity. To enable the rational design of these multifunctional materials, data-driven models will be formulated, incorporating a wide range of composite descriptors including filler composition, shape, volume fraction, solid to liquid filler ratio, and polymer matrix. In the modeling framework, data-driven embeddings will be utilized to reduce the design search space and expedite the discovery of optimal parameters. Predicting the failure of these heterogeneous materials is extremely challenging due to the presence of dissimilar filler phases and their complex microstructures. Therefore, sparse feature selection will be employed to identify the most dominant factors contributing to failure. The new insights will be applied to synthesize composites with designed properties, and the resulting data will be used for model validation and iterative feedback. This research will create a comprehensive data library for soft multifunctional materials, revealing the relationship between their descriptors and mechanical failure while establishing a universal framework for the efficient design of multiphase materials with engineered properties and failure characteristics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

具有混杂填料的聚合物复合材料通过复杂的合成工艺生产，该工艺涉及具有不同形态和物理性质的两种或更多种夹杂物，这通常导致费力、耗时、低产率和昂贵的努力。此外，它们的机械负载能力和对过早失效的敏感性的不确定性阻碍了它们的广泛采用。该奖项支持基础研究，以逆向设计复合材料与固相和液相填料，同时调查他们在大变形下的失败。通过建立一个全面和可解释的机器学习模型，该项目将实现具有目标性能的复合材料的高效和可靠的合成。新知识将促进软多功能复合材料在新兴应用中的利用，如自供电可穿戴电子产品，生物监测系统和软机器人。该奖项还将通过推广计划和创建有关高级力学和人工智能主题的免费教育在线内容来支持多元化劳动力的发展。本研究的目标是对含有固液填料的“混合填料复合材料”进行逆向设计，并研究其在大变形下的失效。嵌入式液相填料增加了多相复合材料力学的复杂性，同时提供了独特的优势，如增强的韧性和导电性。为了使这些多功能材料的合理设计，数据驱动的模型将制定，纳入广泛的复合材料描述符，包括填料组成，形状，体积分数，固液填料比，和聚合物基体。在建模框架中，数据驱动的嵌入将被用来减少设计搜索空间，加快发现最佳参数。由于存在不同的填料相及其复杂的微观结构，预测这些异质材料的失效极具挑战性。因此，将采用稀疏特征选择来识别导致故障的最主要因素。新的见解将用于合成具有设计特性的复合材料，所得数据将用于模型验证和迭代反馈。这项研究将建立一个全面的数据库，为软多功能材料，揭示了它们的描述符和机械故障之间的关系，同时为具有工程特性和故障特征的多相材料的有效设计建立了一个通用框架。该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.