BRITE Fellow: AI-Enabled Discovery and Design of Programmable Material Systems
BRITE 研究员:人工智能支持的可编程材料系统的发现和设计
基本信息
- 批准号:2227641
- 负责人:
- 金额:$ 99.98万
- 依托单位:
- 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:2023-01-01 至 2027-12-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Fellow grant will establish a transformative data-driven design framework enabled by artificial intelligence (AI) for the real-time digital design and fabrication of programmable material systems (PMS). PMS are emerging architectural structures made of smart materials that are responsive to external stimuli (e.g., stress, thermal inputs, chemical changes, light, and magnetic fields) and that can be programmed to transform between multiple functional states. PMS have far-reaching, societally impactful applications, including surgical robots, (bio)sensors, deployable satellites, mechanical computing, and water and energy harvesting. The design of PMS is still in its infancy, however, due to the complex underlying physics and high dimensionality associated with the design of spatially varying materials, architectures, and stimuli. To address these challenges, this project seeks to integrate disruptive technologies across the multidisciplinary domains of design, mechanics, manufacturing, materials, and data science to create a new AI-enabled PMS digital design platform. In collaboration with Minority Serving Institutions (MSIs), research results will be integrated into AI literacy programs and activities for K-12 and college students. A wide range of diversity, equity, and inclusion activities will also be accomplished, with emphasis on mentoring and collaboration with junior faculty from underrepresented groups and enhancing access to STEM pathways for underrepresented minority students.The research objective of this project is to establish a novel data-driven design framework called ALGO (Acquire-Learn-Generate-Optimize) that will accelerate the co-design of materials (M), architectures (A), and stimuli (S) in programmable material systems (PMS). The specific goals are to: 1) Create a shared PMS data resource to bridge knowledge gaps across multiple disciplines and domains; 2) Develop novel statistical and AI-based learning techniques to understand complex M-A-S interactions and derive transferrable PMS design rules; and 3) Employ a “building block” approach to create multiscale design strategies that combine machine learning with topology optimization to achieve superior computational efficiency and unprecedented performance for real-time PMS digital design. This research will provide a paradigm shift that transforms existing techniques limited to the design of single-material periodic structures into scalable data-driven design of programmable multi-material systems with heterogenous materials and topological architectures. While the PMS design testbeds used in this research will be focused on Shape Transformation, Wave Guiding, and Surface Engineering, the AI-enhanced learning and design automation techniques developed here will benefit a wide range of physics-driven science and engineering domains. Exploiting heterogeneity and programmability in material systems through intelligent design will have long-lasting impacts on US competitiveness in developing innovative, lightweight, portable, economic, and sustainable products.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.
这项促进工程变革和公平进步的研究思路(BRITE)研究员赠款将建立一个由人工智能(AI)支持的变革性数据驱动设计框架,用于可编程材料系统(PMS)的实时数字设计和制造。PMS是由智能材料制成的新兴建筑结构,其对外部刺激(例如,应力、热输入、化学变化、光和磁场),并且可以被编程以在多个功能状态之间转换。PMS具有深远的社会影响力应用,包括手术机器人,(生物)传感器,可部署卫星,机械计算以及水和能量收集。然而,由于与空间变化材料、架构和刺激设计相关的复杂基础物理和高维度,PMS的设计仍处于起步阶段。为了应对这些挑战,该项目旨在整合设计,机械,制造,材料和数据科学等多学科领域的颠覆性技术,以创建一个新的支持AI的PMS数字设计平台。与少数民族服务机构(MSIs)合作,研究成果将被整合到K-12和大学生的AI扫盲计划和活动中。此外,还将开展一系列多样性、公平性和包容性活动,重点是与代表性不足的群体的初级教师进行指导和合作,并加强代表性不足的少数民族学生进入STEM途径的机会。(获取-学习-生成-优化),这将加速可编程材料系统(PMS)中材料(M),架构(A)和刺激(S)的协同设计。 具体目标是:1)创建共享的PMS数据资源,以弥合多个学科和领域的知识差距; 2)开发新的统计和基于AI的学习技术,以理解复杂的M-A-S交互,并导出可转移的PMS设计规则;(3)采用“积木”一种创建多尺度设计策略的方法,该策略将联合收割机机器学习与拓扑优化相结合,以实现上级计算效率和前所未有的实时PMS数字设计的性能。这项研究将提供一个范式转变,将现有的技术局限于单一材料的周期性结构的设计到可扩展的数据驱动的可编程多材料系统的设计与异质材料和拓扑结构。 虽然本研究中使用的PMS设计测试平台将专注于形状转换,波导和表面工程,但这里开发的人工智能增强学习和设计自动化技术将使广泛的物理驱动的科学和工程领域受益。通过智能设计开发材料系统的异质性和可编程性将对美国在开发创新、轻量、便携、经济和可持续产品方面的竞争力产生长期影响。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
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Wei Chen其他文献
Nb-doped layered FeNi phosphide nanosheets for highly efficient overall water splitting under high current densities
掺铌层状 FeNi 磷化物纳米片可在高电流密度下实现高效的整体水分解
- DOI:
10.1039/d1ta00372k - 发表时间:
2021-04 - 期刊:
- 影响因子:0
- 作者:
Shuting Wen;Guangliang Chen;Wei Chen;Xianhui Zhang - 通讯作者:
Xianhui Zhang
Research on the Complexity of Information System Development
信息系统开发复杂性研究
- DOI:
10.2991/meici-15.2015.208 - 发表时间:
2015 - 期刊:
- 影响因子:0.9
- 作者:
Wei Chen;Yan Zhang - 通讯作者:
Yan Zhang
A real-time multi-constraints obstacle avoidance method based on LiDAR
一种基于LiDAR的实时多约束避障方法
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Wei Chen;Jian Sun;Weishuo Li;Dapeng Zhao - 通讯作者:
Dapeng Zhao
Anionic Ln–MOF with tunable emission for heavy metal ion capture and l-cysteine sensing in serum
具有可调谐发射功能的阴离子 Ln−MOF,用于血清中的重金属离子捕获和 L-半胱氨酸传感
- DOI:
10.1039/c9ta13932j - 发表时间:
2020-03 - 期刊:
- 影响因子:11.9
- 作者:
Tiancheng Sun;Ruiqing Fan;Rui Xiao;Tingfeng Xing;Mingyue Qin;Yaqi Liu;Sue Hao;Wei Chen;Yulin Yang - 通讯作者:
Yulin Yang
Ingenious introduction of aminopropylimidazole to tune the hydrophobic selectivity of dodecyl-bonded stationary phase for environmental organic pollutants
巧妙引入氨基丙基咪唑来调节十二烷基键合固定相对环境有机污染物的疏水选择性
- DOI:
10.1016/j.microc.2022.107933 - 发表时间:
2022-09 - 期刊:
- 影响因子:4.8
- 作者:
Yan Wu;Panpan Cao;Yanhao Jiang;Yanjuan Liu;Yuefei Zhang;Wei Chen;Zhengwu Bai;Sheng Tang - 通讯作者:
Sheng Tang
Wei Chen的其他文献
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{{ truncateString('Wei Chen', 18)}}的其他基金
CAREER: First-principles Predictive Understanding of Chemical Order in Complex Concentrated Alloys: Structures, Dynamics, and Defect Characteristics
职业:复杂浓缩合金中化学顺序的第一原理预测性理解:结构、动力学和缺陷特征
- 批准号:
2415119 - 财政年份:2024
- 资助金额:
$ 99.98万 - 项目类别:
Continuing Grant
Collaborative Research: EAGER: SSMCDAT2023: Data-driven Predictive Understanding of Oxidation Resistance in High-Entropy Alloy Nanoparticles
合作研究:EAGER:SSMCDAT2023:数据驱动的高熵合金纳米颗粒抗氧化性预测理解
- 批准号:
2334385 - 财政年份:2023
- 资助金额:
$ 99.98万 - 项目类别:
Standard Grant
Collaborative Research: I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery
合作研究:I-AIM:用于多尺度材料发现的可解释增强智能
- 批准号:
2404816 - 财政年份:2023
- 资助金额:
$ 99.98万 - 项目类别:
Standard Grant
Collaborative Research: Microscopic Mechanism of Surface Oxide Formation in Multi-Principal Element Alloys
合作研究:多主元合金表面氧化物形成的微观机制
- 批准号:
2219489 - 财政年份:2022
- 资助金额:
$ 99.98万 - 项目类别:
Standard Grant
Collaborative Research: A Hierarchical Multidimensional Network-based Approach for Multi-Competitor Product Design
协作研究:基于分层多维网络的多竞争对手产品设计方法
- 批准号:
2005661 - 财政年份:2020
- 资助金额:
$ 99.98万 - 项目类别:
Standard Grant
CAREER: First-principles Predictive Understanding of Chemical Order in Complex Concentrated Alloys: Structures, Dynamics, and Defect Characteristics
职业:复杂浓缩合金中化学顺序的第一原理预测性理解:结构、动力学和缺陷特征
- 批准号:
1945380 - 财政年份:2020
- 资助金额:
$ 99.98万 - 项目类别:
Continuing Grant
Collaborative Research: I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery
合作研究:I-AIM:用于多尺度材料发现的可解释增强智能
- 批准号:
1940114 - 财政年份:2019
- 资助金额:
$ 99.98万 - 项目类别:
Standard Grant
Collaborative Research: Framework: Data: HDR: Nanocomposites to Metamaterials: A Knowledge Graph Framework
合作研究:框架:数据:HDR:纳米复合材料到超材料:知识图框架
- 批准号:
1835782 - 财政年份:2018
- 资助金额:
$ 99.98万 - 项目类别:
Standard Grant
RUI: Poly (vinyl alcohol) Thin Film Dewetting by Controlled Directional Drying
RUI:通过受控定向干燥进行聚(乙烯醇)薄膜去湿
- 批准号:
1807186 - 财政年份:2018
- 资助金额:
$ 99.98万 - 项目类别:
Standard Grant
Collaborative Research: Concurrent Design of Quasi-Random Nanostructured Material Systems (NMS) and Nanofabrication Processes using Spectral Density Function
合作研究:使用谱密度函数并行设计准随机纳米结构材料系统(NMS)和纳米制造工艺
- 批准号:
1662435 - 财政年份:2017
- 资助金额:
$ 99.98万 - 项目类别:
Standard Grant
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