DMREF: Data Driven Discovery of Conjugated Polyelectrolytes for Neuromorphic Computing

DMREF:用于神经形态计算的共轭聚电解质的数据驱动发现

基本信息

  • 批准号:
    1922042
  • 负责人:
  • 金额:
    $ 174.96万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-10-01 至 2024-09-30
  • 项目状态:
    已结题

项目摘要

DMREF: Data Driven Discovery of Conjugated Polyelectrolytes for Neuromorphic ComputingNon-Technical Description: As a potentially disruptive technology, neuromorphic computing breaks away from the current performance-limiting conventional computer architectures (i.e. von Neumann paradigm) by developing biologically inspired computational devices with artificial intelligence capabilities. Organic electronic materials have recently emerged as attractive alternatives to inorganic counterparts in neuromorphic computing owing to their low-energy switching, excellent tunability, low fabrication costs, and biocompatibility. In this project, we will establish a collaborative, multidisciplinary and data-centric research program to accelerate the discovery of novel conjugated polyelectrolytes (CPEs) with chemical structures tailored for the demands of neuromorphic computing. The project will bear direct impact on applications ranging from neuromorphic computing, to energy generation (photovoltaic and thermoelectric materials), sensing, robotics, and pathogen mitigation. The project will also provide cutting-edge educational and training opportunities to students and postdoctoral fellows who will gain valuable experience in data science, materials informatics, and data driven material research. The PIs are fully committed to broadening participation and enhancing diversity in materials research and education by strengthening opportunities for underrepresented groups in STEM fields. Exposing the underrepresented groups to data centric research and education is a key component of the project. A set of courses will be developed at various levels in both participating institutions to prepare the students and postdoctoral fellows for the proposed research. The PIs will reach out to local high schools and community colleges through targeted recruitment, workshops, and summer camps for high school teachers.Technical Description: The proposed research will significantly accelerate the discovery of CPE materials specifically designed for neuromorphic computing. The research effort integrates high-throughput computation, machine learning, multiscale modeling, chemical synthesis, and materials and device characterization executed in a "closed loop" manner. The team will construct the first comprehensive database dedicated to CPEs, which includes a collection of structural, elastic, vibrational, electronic, dielectric, and energetic properties for over ten thousand CPEs. Based on the CPE database, the team will explore the correlations between the materials properties and formulate a set of molecular design rules. Materials characterization will be performed to validate the design rules and once validated, they will provide guidance for predictions of promising CPEs. Based on the predictions, the team will synthesize the most promising CPEs and examine their performance in the neuromorphic devices. The project has three deliverables: (1) The first comprehensive CPE database. (2) A fundamental understanding on how the backbone structure and adjacent electrostatic forces control CPE properties and a set of design rules for accelerated materials discovery. (3) A set of highly optimized and promising CPE structures for neuromorphic and optoelectronic applications. Successful completion of the project not only impacts neuromorphic computing, but also research areas, such as photovoltaics, light-emitting diodes, thermoelectrics, sensors and robotics. The potential to affect transformative breakthroughs in the rational design of neuromorphic and organic electronic materials makes a compelling case for the project.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.
DMREF:非技术描述:作为一种潜在的颠覆性技术,神经形态计算通过开发具有人工智能能力的生物启发计算设备,摆脱了当前性能限制的传统计算机架构(即冯诺依曼范式)。有机电子材料由于其低能量开关、优异的可调谐性、低制造成本和生物相容性,最近成为神经形态计算中无机对应物的有吸引力的替代品。在这个项目中,我们将建立一个协作的,多学科的和以数据为中心的研究计划,以加速发现新的共轭聚电解质(CPE),其化学结构适合神经形态计算的需求。该项目将对从神经形态计算到能源发电(光伏和热电材料)、传感、机器人和病原体缓解等应用产生直接影响。该项目还将为学生和博士后研究员提供尖端的教育和培训机会,他们将获得数据科学,材料信息学和数据驱动材料研究方面的宝贵经验。PI完全致力于通过加强STEM领域代表性不足的群体的机会来扩大参与并增强材料研究和教育的多样性。将代表性不足的群体暴露于以数据为中心的研究和教育是该项目的关键组成部分。将在两个参与机构的各个层次上开发一套课程,为学生和博士后研究员进行拟议的研究做好准备。PI将通过有针对性的招聘,研讨会和高中教师夏令营来接触当地的高中和社区大学。技术描述:拟议的研究将显着加速专门为神经形态计算设计的CPE材料的发现。研究工作集成了高通量计算,机器学习,多尺度建模,化学合成以及以“闭环”方式执行的材料和设备表征。该团队将构建第一个专门用于CPE的综合数据库,其中包括一万多个CPE的结构,弹性,振动,电子,介电和能量特性的集合。基于CPE数据库,该团队将探索材料性能之间的相关性,并制定一套分子设计规则。将进行材料表征以验证设计规则,一旦验证,它们将为有前途的CPE的预测提供指导。根据预测,该团队将合成最有前途的CPE,并检查它们在神经形态设备中的性能。该项目有三个成果:(1)第一个全面的CPE数据库。(2)对骨架结构和相邻静电力如何控制CPE性能的基本理解,以及一套加速材料发现的设计规则。(3)一组高度优化和有前途的CPE结构,用于神经形态学和光电应用。该项目的成功完成不仅会影响神经形态计算,而且会影响研究领域,如光电学,发光二极管,热电学,传感器和机器人技术。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(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Data driven discovery of conjugated polyelectrolytes for optoelectronic and photocatalytic applications
  • DOI:
    10.1038/s41524-021-00541-5
  • 发表时间:
    2021-05
  • 期刊:
  • 影响因子:
    9.7
  • 作者:
    Yangyang Wan;F. Ramírez;Xu Zhang;Thuc‐Quyen Nguyen;G. Bazan;G. Lu
  • 通讯作者:
    Yangyang Wan;F. Ramírez;Xu Zhang;Thuc‐Quyen Nguyen;G. Bazan;G. Lu
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Gang Lu其他文献

Sperm cryopreservation of the endangered red spotted grouper, Epinephelus akaara, with a special emphasis on membrane lipids
濒临灭绝的红斑石斑鱼(赤点石斑鱼)的精子冷冻保存,特别注重膜脂
  • DOI:
    10.1016/j.aquaculture.2011.05.025
  • 发表时间:
    2011-07
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Kai Che;Changjiang Huang;Qiongshan Fang;Hansheng Wang;Gang Lu;Jing Liu;Enhui Zhao;Qiaoxiang Dong;Qiutao He
  • 通讯作者:
    Qiutao He
Improved photochromic properties on viologen based inorganic-organic hybrids by using conjugated substituents as electron donors and stabilizers
通过使用共轭取代基作为电子供体和稳定剂改善基于紫罗碱的无机-有机杂化物的光致变色性能
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Rong-Guang Lin;Gang Xu;Ming-Sheng Wang;Gang Lu;Pei-Xin Li;Guo-Cong Guo
  • 通讯作者:
    Guo-Cong Guo
Monoclonal Antibody 6D2 Against delta 6 Fatty Acid Desaturase of Mortierella isabellina
伊莎贝氏被孢霉δ6脂肪酸去饱和酶单克隆抗体6D2
Comparison and Analysis of Flow Features at the Packet Level for Traffic Classification
流分类中报文级别的流特征对比分析
Amagmatic subduction produced by mantle serpentinization and oceanic crust delamination
地幔蛇纹石化和洋壳拆沉作用产生的非岩浆俯冲作用
  • DOI:
    10.1029/2019gl086257
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
    Jianfeng Yang;Gang Lu;Tong Liu;Yang Li;Kun Wang;Xinxin Wang;Baolu Sun;Manuele Faccenda;Liang Zhao
  • 通讯作者:
    Liang Zhao

Gang Lu的其他文献

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{{ truncateString('Gang Lu', 18)}}的其他基金

PREM: Partnership between CSUN and Princeton for Quantum Materials
PREM:CSUN 与普林斯顿大学在量子材料方面的合作
  • 批准号:
    1828019
  • 财政年份:
    2018
  • 资助金额:
    $ 174.96万
  • 项目类别:
    Continuing Grant
PREM - Computational Research and Education for Emergent Materials
PREM - 新兴材料的计算研究和教育
  • 批准号:
    1205734
  • 财政年份:
    2012
  • 资助金额:
    $ 174.96万
  • 项目类别:
    Continuing Grant
MRI-R2: Acquisition of a Beowulf Cluster for Computational Materials Research and Education
MRI-R2:获取 Beowulf 集群用于计算材料研究和教育
  • 批准号:
    0958596
  • 财政年份:
    2010
  • 资助金额:
    $ 174.96万
  • 项目类别:
    Standard Grant
Quantitative Characterisation of Flame Radical Emissions for Combustion Optimisation through Spectroscopic Imaging
通过光谱成像定量表征燃烧优化的火焰自由基发射
  • 批准号:
    EP/G002398/1
  • 财政年份:
    2009
  • 资助金额:
    $ 174.96万
  • 项目类别:
    Research Grant

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Collaborative Research: DMREF: Data-Driven Discovery of the Processing Genome for Heterogenous Superalloy Microstructures
合作研究:DMREF:异质高温合金微结构加工基因组的数据驱动发现
  • 批准号:
    2323936
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    2023
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Collaborative Research: DMREF: Data-Driven Prediction of Hybrid Organic-Inorganic Structures
合作研究:DMREF:混合有机-无机结构的数据驱动预测
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合作研究:DMREF:混合有机-无机结构的数据驱动预测
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