CAREER: Developing Wavefunction-based Quantum Chemistry for Solids

职业：开发基于波函数的固体量子化学

• 批准号: 2045046
• 负责人: James Shepherd
• 金额: $ 64.83万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045046&HistoricalAwards=false
• 关键词: CAREER; Developing; Wavefunction; based; Quantum

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, and the Established Program to Stimulate Competitive Research (EPSCoR) Dr. James Shepherd of the University of Iowa will be developing high-accuracy ground-state wavefunction electronic structure methods for the prediction of bulk properties of solids. Novel quantum materials have the potential to drive technological change due to the unique properties they possess as a result of the specific nature of their electronic structure. Examples of these materials include catalysts and semiconductors. When computational chemists are able to faithfully model the electronic structure of these compounds, they can contribute to the rational design of materials alongside experimentalists. The Shepherd research group is pursuing tools that enable quicker and more accurate such calculations. These tools are expected to advance our theoretical understanding of how electrons interact in solids. The Shepherd research group is also developing a course-based undergraduate research program using an evidence-based design philosophy. This program will train undergraduate STEM (Science, Technology, Engineering and Mathematics) majors in the computer-aided design of new materials commensurate with the uptick in interest in computationally guided materials design in academia and industry. Additionally, the program will seek to improve retention of students in STEM majors, especially of those students from underrepresented groups.In this project, Dr. James Shepherd and his research group at the University of Iowa are working to improve high accuracy computational chemistry methods, such as coupled cluster theory and full configuration interaction quantum Monte Carlo. The widespread adoption of these methods is prevented by finite size errors, which arise from trying to simulate real systems with a limited number of atoms. The Shepherd research group is systematically studying the physical origin of these errors; removing these errors for wavefunction-based quantum chemistry; and leveraging these technologies to study catalyst and semiconductor design.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.

在化学系的化学理论、模型和计算方法项目以及刺激竞争性研究的既定项目（EPSCoR）的支持下，爱荷华州大学的James Shepherd博士将开发高精度基态波函数电子结构方法，用于预测固体的整体性质。新型量子材料具有推动技术变革的潜力，因为它们具有独特的性质，这是由于它们的电子结构的特定性质。这些材料的实例包括催化剂和半导体。当计算化学家能够忠实地模拟这些化合物的电子结构时，他们可以与实验学家一起为材料的合理设计做出贡献。Shepherd研究小组正在寻求能够更快，更准确地进行此类计算的工具。这些工具有望推进我们对电子如何在固体中相互作用的理论理解。Shepherd研究小组还正在使用基于证据的设计理念开发一个基于课程的本科研究项目。该计划将培养本科STEM（科学，技术，工程和数学）专业的新材料计算机辅助设计，与学术界和工业界对计算指导材料设计的兴趣上升相称。此外，该计划将寻求提高学生在STEM专业的保留，特别是那些来自代表性不足的群体的学生。在这个项目中，爱荷华州大学的James Shepherd博士和他的研究小组正在致力于改进高精度的计算化学方法，如耦合团簇理论和全组态相互作用量子蒙特卡罗。这些方法的广泛采用受到有限尺寸误差的阻碍，这些误差来自于试图用有限数量的原子来模拟真实的系统。Shepherd研究小组正在系统地研究这些错误的物理根源;消除基于波函数的量子化学的这些错误;并利用这些技术研究催化剂和半导体设计。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

Machine learning for a finite size correction in periodic coupled cluster theory calculations

周期性耦合簇理论计算中有限尺寸修正的机器学习

• DOI: 10.1063/5.0086580
• 发表时间: 2022
• 期刊: The Journal of Chemical Physics
• 作者: Weiler, Laura;Mihm, Tina;Shepherd, James J.
• 通讯作者: Shepherd, James J.