CAREER: Many-Body Green's Function Framework for Materials Spectroscopy

职业:材料光谱的多体格林函数框架

基本信息

  • 批准号:
    2337991
  • 负责人:
  • 金额:
    $ 65万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-01-01 至 2028-12-31
  • 项目状态:
    未结题

项目摘要

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry, Dr. Tianyu Zhu of Yale University is developing high accuracy theoretical methods for simulating spectroscopic properties of solid state materials. Computational modeling of how light interacts with materials is important for advancing technological applications in optoelectronics design, solar energy conversion, catalysis, and in semiconductor development. However, current computational tools have limited accuracy and efficiency for investigating large scale many-electron materials, hindering our capability to tune and control their electronic properties and chemical reactivity. Dr. Zhu and his group will develop and leverage new ideas in quantum chemistry, condensed matter physics, and data science, to create a reliable and efficient toolbox for modeling light-matter interactions in complex materials. These new methods will be incorporated into the open-source PySCF software package to benefit the broader scientific community. Through this program, Dr. Zhu and his team will develop a hands-on computer game demonstration of organic light-emitting materials design through Yale University’s outreach programs for K-12 students. He will also create a summer computational chemistry workshop and summer research internships targeting underrepresented high school students, as well as organize a guest lecture series to demystify computational chemistry for undergraduate students in chemistry.This research is directed at developing a workable many body Green’s function based on electronic structure methods for simulating charged and excitonic excitations in condensed matter systems, which is crucial for understanding electron correlation physics and energy transfer dynamics in materials. Dr. Zhu and his team will formulate a Green’s function quantum embedding method that enables the use of correlated excited state quantum chemistry tools in simulating photoemission spectra of extended systems, such as at the level of coupled cluster and multi-reference theories. A two particle extension of this method will be further developed to capture electron-hole interactions in describing optical spectra. In addition, the Zhu group will develop a machine learning approach to enable highly efficient, many body Green’s function calculations of molecules and materials. Adopting the established framework, systematic benchmarks on the accuracy of excited-state quantum chemistry methods in predicting valence excitations in weakly and strongly correlated electron materials will be pursued.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.
在化学系化学理论、模型和计算方法 (CTMC) 项目的支持下,耶鲁大学的朱天宇博士正在开发高精度的理论方法,用于模拟固态材料的光谱特性以及光如何相互作用的计算模型。材料对于推进光电子设计、太阳能转换、催化和半导体开发中的技术应用非常重要,但是,当前的计算工具在研究大规模多电子材料方面的准确性和效率有限,阻碍了我们的能力。朱博士和他的团队将开发和利用量子化学、凝聚态物理和数据科学方面的新思想,创建一个可靠、高效的工具箱,用于模拟复杂的光与物质相互作用。这些新方法将被纳入开源 PySCF 软件包中,以造福更广泛的科学界。通过该计划,朱博士和他的团队将开发有机发光材料设计的动手电脑游戏演示。耶鲁大学的外展计划他还将针对 K-12 学生举办夏季计算化学研讨会和暑期研究实习,并组织客座讲座系列,为化学专业的本科生揭开计算化学的神秘面纱。基于电子结构方法的可行的多体格林函数,用于模拟凝聚态物质系统中的带电和激子激发,这对于理解材料中的电子相关物理和能量传递动力学至关重要。朱博士和他的团队将制定一个方法。格林函数量子嵌入方法能够使用相关激发态量子化学工具来模拟扩展系统的光电发射光谱,例如在耦合簇和多参考理论的水平上,该方法的两个粒子扩展将进一步发展到。此外,朱小组将开发一种机器学习方法,以实现分子和材料的高效、多体格林函数计算,采用已建立的框架,建立系统的准确性基准。将采用激发态量子化学方法来预测弱关联和强关联电子材料中的价态激发。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力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

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Tianyu Zhu其他文献

Renewable atom-efficient polyesters and thermosetting resins derived from high oleic soybean oil
由高油酸大豆油衍生的可再生原子效率聚酯和热固性树脂
  • DOI:
    10.1039/c7gc03774k
  • 发表时间:
    2018-03
  • 期刊:
  • 影响因子:
    9.8
  • 作者:
    Shichao Xu;Meghan E. Lamm;Md Anisur Rahman;Xinzhou Zhang;Tianyu Zhu;Zhendong Zhao;Chuanbing Tang
  • 通讯作者:
    Chuanbing Tang
Multilevel optimization framework for hierarchical stiffened shellsaccelerated by adaptive equivalent strategy
自适应等效策略加速的分层加劲壳多级优化框架
  • DOI:
    10.1007/s10443-016-9527-y
  • 发表时间:
  • 期刊:
  • 影响因子:
    2.3
  • 作者:
    Bo Wang;Kuo Tian;Haixin Zhao;Peng Hao;Tianyu Zhu;Ke Zhang;Yunlong Ma
  • 通讯作者:
    Yunlong Ma
Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
在协同过滤中统一图卷积和对比学习
  • DOI:
    10.1145/3637528.3671840
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yihong Wu;Le Zhang;Fengran Mo;Tianyu Zhu;Weizhi Ma;Jian
  • 通讯作者:
    Jian
An Aneurysm Localization Algorithm Based on Faster R-CNN Network for Cerebral Small Vessels
基于Faster R-CNN网络的脑小血管动脉瘤定位算法
Review on core-shell structured cathode for intermediate temperature solid oxide fuel cells
中温固体氧化物燃料电池核壳结构阴极研究进展
  • DOI:
    10.1016/j.ijhydene.2020.06.034
  • 发表时间:
    2020-09
  • 期刊:
  • 影响因子:
    7.2
  • 作者:
    Peng Qiu;Xin Yang;Tianyu Zhu;Shichen Sun;Lichao Jia;Jian Li
  • 通讯作者:
    Jian Li

Tianyu Zhu的其他文献

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