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）项目的支持下，耶鲁大学的朱天宇博士正在开发用于模拟固态材料光谱特性的高精度理论方法。光如何与材料相互作用的计算建模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。然而，目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，阻碍了我们调整和控制其电子性质和化学反应性的能力。朱博士和他的团队将开发和利用量子化学、凝聚态物理学和数据科学方面的新思想，为复杂材料中的光物质相互作用建模创建一个可靠、高效的工具箱。这些新方法将被纳入开源PySCF软件包，以造福更广泛的科学界。通过这个项目，朱博士和他的团队将通过耶鲁大学为K-12学生提供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。他还将创建一个夏季计算化学研讨会和夏季研究实习，针对代表性不足的高中生，以及组织一个客座讲座系列，以揭开神秘的计算化学本科生在化学。这项研究是针对开发一个可行的多体绿色函数的基础上，电子结构方法模拟带电和激子激发凝聚态系统，这对于理解材料中的电子相关物理和能量转移动力学是至关重要的。朱博士和他的团队将制定一个绿色函数量子嵌入方法，使相关激发态量子化学工具能够用于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。将此方法推广到两粒子情形，以捕捉电子-空穴相互作用来描述光谱。此外，朱团队还将开发一种机器学习方法，以实现对分子和材料的高效、多体绿色函数计算。采用已建立的框架，激发态量子化学方法在预测弱相关和强相关电子材料中的价态激发的准确性的系统基准将被追求。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。

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.