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

在化学系化学理论、模型和计算方法(CTMC)计划的支持下，耶鲁大学朱天宇博士正在开发高精度的理论方法来模拟固体材料的光谱性质。光与材料相互作用的计算模型对于推进光电子学设计、太阳能转换、催化和半导体开发等技术应用具有重要意义。然而，目前的计算工具对于研究大规模多电子材料的精度和效率有限，这阻碍了我们调整和控制它们的电子性质和化学反应活性的能力。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的新想法，创建一个可靠而高效的工具箱，用于模拟复杂材料中的光-物质相互作用。这些新方法将被纳入开放源码的PySCF软件包，以造福于更广泛的科学界。通过这个项目，朱博士和他的团队将通过耶鲁大学面向K-12学生的推广项目，开发一个有机发光材料设计的动手电脑游戏演示。他还将创建一个针对未被充分代表的高中生的暑期计算化学研讨会和暑期研究实习，并组织一系列客座讲座，为化学专业的本科生揭开计算化学的神秘面纱。这项研究旨在开发一个可行的基于电子结构方法的多体格林函数，用于模拟凝聚态系统中的带电和激子激发，这对于理解材料中的电子关联物理和能量转移动力学至关重要。朱博士和他的团队将制定一种格林函数量子嵌入方法，使得能够使用关联激发态量子化学工具来模拟扩展系统的光电子能谱，例如在耦合团簇和多参考理论的水平上。这种方法的两粒子扩展将被进一步发展，以捕捉电子-空穴相互作用来描述光谱。此外，朱团队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。采用建立的框架，将追求关于激发态量子化学方法在预测弱关联和强关联电子材料中的价激发的准确性的系统基准。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.