NRI: INT: Self-Assembly of Modular Robots Constructed using DNA: Modeling and Manufacturing Nanostructures with Graph Neural Networks and DNA Origami
NRI:INT:使用 DNA 构建的模块化机器人的自组装:使用图神经网络和 DNA 折纸建模和制造纳米结构
基本信息
- 批准号:2132886
- 负责人:
- 金额:$ 122.01万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-15 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Future micro-scale robotic devices will be integrated in broad aspects of human life, having explicit impact on applications as diverse as non-invasive surgical procedures to advanced electronics manufacturing. However, at very small physical scales it is essentially impossible to manipulate, in a conventional sense, the physical components that are necessary to construct mechanical structures. In addition, it is also very difficult to see or otherwise sense with fidelity mechanisms that are formed at extremely small scales, e.g., the nano-scale. This inability to easily sense nano-scale mechanisms makes it difficult to gather data on the efficacy of different assembly processes or collect feedback data that would be required to control active mechanisms, i.e., to actuate them to induce some desired motion. Therefore, to address these difficulties, this work proposes to develop a synergistic framework that combines ideas related to contemporary top-down and bottom-up manufacturing processes with those from the machine learning (ML), artificial intelligence (AI), and robotics communities to address what we see as the largest current barriers to the practical deployment of future nano-scale robotic systems: 1) manipulating components for assembly, 2) the availability of low-cost, readily available sensing, and 3) actuating the mechanisms once formed. In addition, we propose to make broader contributions to the research community by establishing a framework for archiving multimodal data about nanostructure formation statistics that will be shared with and ideally added to by other researchers. Lastly, from an educational perspective, the PIs have already begun to educate middle school students about artificial intelligence and DNA nanotechnology and intend we further these efforts by introducing a novel macroscale model that can serve as an interactive activity for teaching K-12 students more about the confluence of DNA nanotechnology and artificial intelligence. This work develops a novel framework that optimizes the outcome of physical processes wherein modular nanoscale robots self-assemble from a set of nano submodules. The main contribution of the framework to be developed is to reduce the uncertainty in large-scale self-assembly processes wherein the objective is to create nanoscale superstructures with specific designs. We propose to use DNA origami to create modular components in a nanoscale test bed because DNA origami is an excellent tool for forming different geometric constructs, e.g., a honeycomb-like truss, with subnanometer precision. We intend to use a graph neural network framework to model complex, large-scale self-assembly processes as distributions over a discrete space of modular DNA superstructures that are represented using graphs. We hypothesize that this approach will allow us to optimize the process conditions during the manufacturing trials, such as the number of unique connections between components, thereby maximizing the yield of desired superstructures. Our key contributions include 1) learn to map low dimensional characterization data to a graph-based representation of the corresponding superstructure populations; 2) generate probabilistic graphs that represent the distribution of superstructures formed for an arbitrary set of manufacturing conditions; and, 3) apply optimization techniques to our generative model to find the optimal manufacturing conditions for maximizing the yield of a desired superstructure. In addition, Taylor and Travers plan to leverage their ongoing collaboration that focuses on using global stimuli like magnetic actuation to induce motion in systems constructed using magnetic micro- and nanoparticles to perform preliminary motion studies that will be conducted using chemical and magnetic field actuation.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.
未来的微型机器人设备将融入人类生活的各个方面,对非侵入性外科手术和先进电子制造等各种应用产生明确的影响。 然而,在非常小的物理尺度上,基本上不可能在传统意义上操纵构造机械结构所必需的物理部件。 此外,也很难看到或以其他方式感知以极小尺度形成的保真度机制,例如,纳米尺度。 这种不能容易地感测纳米级机制的能力使得难以收集关于不同组装过程的功效的数据或收集控制主动机制所需的反馈数据,即,来驱动它们以引起一些期望的运动。 因此,为了解决这些困难,这项工作建议开发一个协同框架,将与当代自上而下和自下而上的制造过程相关的想法与机器学习(ML),人工智能(AI)和机器人社区的想法相结合,以解决我们所看到的最大障碍未来纳米级机器人系统的实际部署:1)操纵组件以进行组装,2)低成本、容易获得的感测的可用性,以及3)一旦形成就致动机构。 此外,我们建议通过建立一个框架来归档有关纳米结构形成统计的多模态数据,从而为研究界做出更广泛的贡献,这些数据将与其他研究人员共享并理想地添加到其中。最后,从教育的角度来看,PI已经开始对中学生进行人工智能和DNA纳米技术的教育,并打算通过引入一种新的宏观模型来进一步推动这些努力,该模型可以作为一种互动活动,用于教授K-12学生更多关于DNA纳米技术和人工智能的融合。 这项工作开发了一种新的框架,优化了物理过程的结果,其中模块化纳米机器人自组装从一组纳米子模块。 要开发的框架的主要贡献是减少大规模自组装过程中的不确定性,其中的目标是创建具有特定设计的纳米级超结构。 我们建议使用DNA折纸在纳米级测试床中创建模块化组件,因为DNA折纸是形成不同几何结构的绝佳工具,例如,一个蜂窝状的桁架,具有亚纳米的精度。 我们打算使用图神经网络框架来模拟复杂的大规模自组装过程,将其作为使用图表示的模块化DNA超结构的离散空间上的分布。我们假设,这种方法将使我们能够在制造试验期间优化工艺条件,例如组件之间的独特连接数量,从而最大限度地提高所需上层结构的产量。我们的主要贡献包括:1)学会将低维表征数据映射到相应超结构群体的基于图形的表示; 2)生成概率图,表示针对任意一组制造条件形成的超结构的分布;以及3)将优化技术应用于我们的生成模型,以找到最佳制造条件,从而最大限度地提高所需超结构的产量。 此外,本发明还提供了一种方法,Taylor和Travers计划利用他们正在进行的合作,重点是使用磁致动等全局刺激来诱导使用磁性微电子构建的系统中的运动。和纳米粒子进行初步的运动研究,将使用化学和磁场驱动进行。这一奖项反映了NSF的法定使命,并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Buoyant magnetic milliswimmers reveal design rules for optimizing microswimmer performance
浮力磁性微型游泳器揭示了优化微型游泳器性能的设计规则
- DOI:10.1039/d3nr02846a
- 发表时间:2023
- 期刊:
- 影响因子:6.7
- 作者:Benjaminson, Emma;Imamura, Taryn;Lorenz, Aria;Bergbreiter, Sarah;Travers, Matthew;Taylor, Rebecca E.
- 通讯作者:Taylor, Rebecca E.
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Matthew Travers其他文献
Matthew Travers的其他文献
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{{ truncateString('Matthew Travers', 18)}}的其他基金
S&AS: FND: COLLAB: Probabilistic Underactuated Motion Adaptation
S
- 批准号:
1724000 - 财政年份:2017
- 资助金额:
$ 122.01万 - 项目类别:
Standard Grant
CPS: Small: Geometric Self-Propelled Articulated Micro-Scale Devices
CPS:小型:几何自走式铰接微型装置
- 批准号:
1739308 - 财政年份:2017
- 资助金额:
$ 122.01万 - 项目类别:
Standard Grant
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