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

• 批准号: 2337991
• 负责人: Tianyu Zhu
• 金额: $ 65万
• 依托单位: Yale University
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337991&HistoricalAwards=false
• 关键词: CAREER; Many; Body; Green; Function

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.

在化学理论的支持下，耶鲁大学的Tianyu Zhu博士在化学划分中的模型和计算方法（CTMC）计划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。光如何与材料相互作用的计算建模对于推进光电设计，太阳能转换，催化剂和半导体开发中的技术应用非常重要。但是，当前的计算工具的准确性和效率有限，可用于研究大规模多电子材料，从而阻碍我们调整和控制其电子特性和化学反应性的能力。朱博士及其小组将在量子化学，凝结物理学和数据科学中开发并利用新的想法，以创建一个可靠，有效的工具箱，以建模复杂材料中的光 - 物质相互作用。这些新方法将纳入开源PYSCF软件包中，以使更广泛的科学界受益。通过该计划，朱博士和他的团队将通过耶鲁大学针对K-12学生的外展计划开发动手的计算机游戏演示，以进行有机发光材料设计。 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 exciting exciting in condensed matter systems, which is crucial for understanding electronic correlation physics and energy transfer dynamics在材料中。朱博士和他的团队将形成一种绿色的功能量子嵌入方法，该方法能够使用相关的激发状态量子化学工具模拟扩展系统的光发射光谱，例如在耦合群集和多参考理论的水平上。该方法的两个粒子扩展将进一步开发，以捕获描述光谱的电子孔相互作用。此外，朱小组将开发一种机器学习方法，以实现高效的许多身体绿色的功能计算分子和材料的功能。将采用既定框架，系统的基准，以预测弱和强相关的电子材料中的兴奋状态量子化学方法的准确性。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估NSF的法定任务，并被视为珍贵的支持。