Collaborative Research: Autonomous Computing Materials

合作研究：自主计算材料

• 批准号: 1940152
• 负责人: Rigoberto Hernandez
• 金额: $ 33万
• 依托单位: Johns Hopkins University
• 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=1940152&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.

随着摩尔定律的终结以及基于硅的数据存储的近期极限，最近全球数据的爆炸式增长推动了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，越来越多的eb级数据集由生物科学和工程学科产生，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，以及其他不同的科学领域，包括气候科学、生态学、天文学、海洋学、社会学和气象学等。在这场数据革命中，这些数据集的规模不断增加，需要同时增加可用的计算能力来存储、处理和利用它们，这推动了对革命性的新替代基板和形式的需求，计算和数据存储。与传统的数据存储和计算材料（如硅）不同，人脑提供了一种非凡的感知、存储、检索和计算信息的能力，这是任何人造材料都无法比拟的。在本研究项目中，将探索非传统计算分子系统和材料中类似的信息感知、数据存储、检索和计算模式。这项研究的首要目标是发现革命性的数据存储/检索、传感和计算的新模式，与传统的硅基技术相媲美，以便在数据科学的所有领域广泛地造福社会。五所院校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，以驾驭正在进行的数据革命。这项研究将通过新闻媒体向广大社区传播，并结合高中学生在参与研究实验室的实习机会。来自小鼠大脑时空钙成像的大规模数据集将被记录为基于dna、纳米粒子和声子的二维和三维软硬材料。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、动态条形码纳米粒子网络以及声子二维和三维材料。传感、计算和数据存储/检索将被证明是利用分子网络和材料的化学特性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。这三种原型材料中的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，并具有跨不同数据领域和材料框架扩展的潜力。因此，研究人员的自主计算材料框架将能够将任意“大数据”集编码为各种材料，用于数据存储、传感和计算。该项目最大限度地利用了一个跨数据科学、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，为颠覆性的新计算和数据科学概念的出现提供了机会。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和美国国家科学基金会数学和物理科学理事会化学部门共同支持。该奖项反映了美国国家科学基金会的法定使命，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

Engineered nanoparticle network models for autonomous computing

用于自主计算的工程纳米颗粒网络模型

• DOI: 10.1063/5.0048898
• 发表时间: 2021
• 期刊: The Journal of Chemical Physics
• 作者: Wei, Xingfei;Zhao, Yinong;Zhuang, Yi;Hernandez, Rigoberto
• 通讯作者: Hernandez, Rigoberto

Autonomous Computing Materials

• DOI: 10.1021/acsnano.0c09556
• 发表时间: 2021-02-26
• 期刊: ACS NANO
• 影响因子: 17.1
• 作者: Bathe, Mark;Hernandez, Rigoberto;Neogi, Sanghamitra
• 通讯作者: Neogi, Sanghamitra