AI Institute: Planning: Novel Neural Architectures for 4D Materials Science

AI 研究所：规划：4D 材料科学的新型神经架构

• 批准号: 2020277
• 负责人: Yang Jiao
• 金额: $ 50万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020277&HistoricalAwards=false
• 关键词: AI; Institute; Planning; Novel; Neural

Non-technical Description: High-fidelity predictive modeling of complex materials under extreme conditions (high temperature, high stress, corrosive environment etc.) is crucial for accelerating material design and optimization to address the pressing challenges in our world. This project will aim to leverage both fundamental and use-inspired artificial intelligence (AI) research, coupled with cutting-edge experiments, to revolutionize and transform traditional materials science and engineering (MSE). The novel approach, rooted in the fundamental principle in MSE, that microstructure controls properties, focuses on the development of novel neural architectures that naturally capture the physical causal relations across key microstructural features at multiple length and time scales for predictive modeling and optimal material design. The methodologies and experimental frameworks for constructing novel physics-based learning models developed in this project will be applied to a variety of compelling problems in complex material systems including ceramics, metals and metallic alloys, composites, and porous materials. It is expected that this project will impact many areas including aerospace, microelectronics, petroleum industry, and consumer products. Technical Description: The theme of this institute involves the development of revolutionary approaches enabled by fundamental and use-inspired AI research, coupled with 4D X-ray microtomography and correlative microscopy, to develop and understand structure-property relationships in vastly different materials systems for both predictively modeling and optimal material design. The goal of the institute will be to accelerate converging research on new learning theories, experimentation methodologies, and validation protocols that will facilitate scientific modeling of the evolutionary and hierarchical structure-property mappings of complex materials systems. In this planning project, researchers mathematically formulate the ubiquitous challenges in modeling complex material structure-property mappings across critical application domains (metals and metallic alloys, multi-functional composites, porous geo-materials, nuclear fuels, etc.), demonstrate the necessity and preliminary feasibility of machine learning and AI in addressing these challenges, and correlate with 4D experiments through x-ray microtomography and correlative microscopy. A consortium of industrial collaborators will be developed to transfer the fundamental knowledge from this program knowledge into practical solutions and to educate a new class of skilled practitioners in the workforce. This project will inspire one to re-think the utility of machine learning in materials science: From knowledge-agnostic feature learning to reasoning mechanisms adaptive to domain-specific knowledge. It will provide the key infrastructure for potential automated materials characterization, research, and discovery.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.

非技术描述：复杂材料在极端条件下（高温、高应力、腐蚀环境等）的高保真预测建模对于加速材料设计和优化以应对我们世界的紧迫挑战至关重要。该项目旨在利用基础和使用启发的人工智能（AI）研究，再加上尖端实验，彻底改变和改造传统的材料科学与工程（MSE）。这种新方法植根于MSE的基本原理，即微结构控制性能，专注于开发新的神经架构，这些架构可以自然地捕获多个长度和时间尺度上关键微结构特征之间的物理因果关系，用于预测建模和最佳材料设计。在这个项目中开发的用于构建新的基于物理的学习模型的方法和实验框架将应用于复杂材料系统中的各种引人注目的问题，包括陶瓷，金属和金属合金，复合材料和多孔材料。预计该项目将影响许多领域，包括航空航天，微电子，石油工业和消费品。 技术说明：该研究所的主题涉及通过基础和使用启发的人工智能研究实现的革命性方法的开发，再加上4D X射线显微断层扫描和相关显微镜，以开发和理解不同材料系统中的结构-性能关系，用于预测建模和最佳材料设计。该研究所的目标将是加速对新的学习理论，实验方法和验证协议的融合研究，这将有助于复杂材料系统的进化和分层结构-属性映射的科学建模。在这个规划项目中，研究人员用数学方法制定了在关键应用领域（金属和金属合金，多功能复合材料，多孔地质材料，核燃料等）中建模复杂材料结构-属性映射的普遍挑战，展示了机器学习和人工智能在应对这些挑战方面的必要性和初步可行性，并通过X射线显微断层扫描和相关显微镜与4D实验相关联。将发展一个工业合作者联盟，将该计划的基础知识转化为实际解决方案，并教育一批新的熟练从业人员。该项目将启发人们重新思考机器学习在材料科学中的应用：从知识不可知的特征学习到适应特定领域知识的推理机制。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Quantifying Microstructural Evolution via Time-Dependent Reduced-Dimension Metrics Based on Hierarchical n-Point Polytope Functions

通过基于分层 n 点多面体函数的时变降维度量来量化微观结构演化

• DOI: 10.1103/physreve.105.025306
• 发表时间: 2022
• 期刊: Physical Review
• 作者: Chen, P.;Raghavan, R;Zheng, Y;Li, H.;Ankit, K.;Jiao, Y
• 通讯作者: Jiao, Y

Correlation-function-based microstructure design of alloy-polymer composites for dynamic dry adhesion tuning in soft gripping

• DOI: 10.1063/5.0082515
• 发表时间: 2022-03
• 期刊: Journal of Applied Physics
• 影响因子: 3.2
• 作者: Yaopengxiao Xu;Pei-En Chen;Hechao Li;Wenxiang Xu;Yi Ren;W. Shan;Yang Jiao

Sudoku-Inspired High-Shannon-Entropy Alloys

• DOI: 10.1016/j.actamat.2021.117556
• 发表时间: 2021-10
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: H. Zhuang

Data-Driven Learning of Three-Point Correlation Functions as Microstructure Representations

作为微观结构表示的三点相关函数的数据驱动学习

• 发表时间: 2022
• 期刊: Acta materialia
• 影响因子: 9.4
• 作者: Cheng, Sheng;Jiao, Yang;Ren, Yi
• 通讯作者: Ren, Yi

Robust Bi-continuous Metal-Elastomer Foam Composites with Highly Tunable Stiffness

具有高度可调刚度的坚固双连续金属弹性体泡沫复合材料

• 发表时间: 2022
• 期刊: Advanced engineering materials
• 影响因子: 3.6
• 作者: Sharifi, S.;Nasab, A. M.;Chen, P.;Liao, Y.;Jiao, Y.;Shan, W
• 通讯作者: Shan, W