Collaborative Research: Autonomous Computing Materials

合作研究：自主计算材料

• 批准号: 1940231
• 负责人: Mark Bathe
• 金额: $ 33.42万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940231&HistoricalAwards=false
• 关键词: Collaborative; Research; Autonomous; Computing; Materials

The recent explosion in worldwide data together with the end of Moore's Law and the near-term limits of silicon-based data storage being reached are driving an urgent need for alternative forms of computing and data storage/retrieval platforms. In particular, exabyte-scale datasets are increasingly being generated by the biological sciences and engineering disciplines including genomics, transcriptomics, proteomics, metabolomics, and high-resolution imaging, as well as disparate other scientific fields including climate science, ecology, astronomy, oceanography, sociology, and meteorology, amongst others. In this data revolution, the continuously increasing size of these datasets requires a concomitant increase in available computational power to store, process, and harness them, which is driving a need for revolutionary new, alternative substrates for, and forms of, computing and data storage. Unlike traditional data storage and computing materials such as silicon, the human brain offers a remarkable ability to sense, store, retrieve, and compute information in a manner that is unrivaled by any human-made material. In this research project, analogous modes of information sensing, data storage, retrieval, and computation will be explored in non-traditional computing molecular systems and materials. The over-arching goal of the research is to discover revolutionary new modes of data storage/retrieval, sensing, and computation that rival conventional silicon-based technology, for deployment to benefit society broadly across all domains of data science. Graduate students and postdocs across five institutions will be trained and mentored in a highly interdisciplinary manner to attain this goal and prepare the next-generation of data scientists, chemists, physicists, and engineers to harness the ongoing data revolution. The research will be disseminated to a broad community through news outlets and integration of high school student internships in participating research laboratories. Large-scale datasets from spatial-temporal calcium imaging of the mouse brain will be recorded into DNA-based, nanoparticle-based, and phononic 2D and 3D soft and hard materials. Continuous spatial-temporal data will first be transformed into discrete data for mapping onto DNA-conjugated fluorophore networks, dynamic barcoded nanoparticle networks, and phononic 2D and 3D materials. Sensing, computation, and data storage/retrieval will be demonstrated as proofs-of-principle in exploiting the chemical properties of molecular networks and materials to recover the encoded neuronal datasets and their sensing and computing processes. Success with any of these three prototypical materials would revolutionize the ability to encode arbitrarily complex, large-scale datasets into complex molecular systems, with the potential to scale across diverse data domains and materials frameworks. The investigators' Autonomous Computing Materials framework will thereby enable the encoding of arbitrary "big data" sets into diverse materials for data storage, sensing, and computing. This project maximizes opportunities for disruptive new computing and data science concepts to emerge from a multi-disciplinary, collaborative team spanning data science, neuroscience, materials science, chemistry, physics, and biological engineering. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR and the Division of Chemistry within the NSF Directorate of Mathematical and Physical Sciences.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.

最近全球数据的爆炸式增长、摩尔定律的终结以及硅基数据存储的近期极限正在推动对替代形式的计算和数据存储/检索平台的迫切需求。特别是，艾字节级数据集越来越多地由生物科学和工程学科（包括基因组学、转录组学、蛋白质组学、代谢组学和高分辨率成像）以及不同的其他科学领域（包括气候科学、生态学、天文学、海洋学、社会学、气象学等。在这场数据革命中，这些数据集的规模不断增加，需要随之增加存储、处理和利用它们的可用计算能力，这推动了对计算和数据存储的革命性的新的替代基质和形式的需求。与硅等传统数据存储和计算材料不同，人脑具有感知、存储、检索和计算信息的非凡能力，其方式是任何人造材料都无法比拟的。在该研究项目中，将在非传统计算分子系统和材料中探索信息传感、数据存储、检索和计算的类似模式。该研究的首要目标是发现与传统硅基技术相媲美的革命性的数据存储/检索、传感和计算新模式，并进行部署，以在数据科学的所有领域广泛造福社会。五个机构的研究生和博士后将以高度跨学科的方式接受培训和指导，以实现这一目标，并为下一代数据科学家、化学家、物理学家和工程师做好准备，以利用正在进行的数据革命。该研究将通过新闻媒体和高中生在参与研究实验室的实习活动向更广泛的社区传播。小鼠大脑时空钙成像的大规模数据集将被记录到基于 DNA、基于纳米颗粒的声子 2D 和 3D 软硬材料中。连续的时空数据将首先转换为离散数据，用于映射到 DNA 共轭荧光团网络、动态条形码纳米颗粒网络以及声子 2D 和 3D 材料上。传感、计算和数据存储/检索将作为利用分子网络和材料的化学性质来恢复编码的神经元数据集及其传感和计算过程的原理证明。这三种原型材料中任何一种的成功都将彻底改变将任意复杂的大规模数据集编码为复杂分子系统的能力，并具有跨不同数据域和材料框架扩展的潜力。研究人员的自主计算材料框架将能够将任意“大数据”集编码为用于数据存储、传感和计算的不同材料。该项目最大限度地增加了颠覆性新计算和数据科学概念的机会，使涵盖数据科学、神经科学、材料科学、化学、物理和生物工程的多学科协作团队涌现。该项目是美国国家科学基金会利用数据革命 (HDR) 大创意活动的一部分，由 HDR 和 NSF 数学和物理科学理事会化学部共同支持。该奖项反映了 NSF 的法定使命，并得到了通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。