Electronic Structure Calculations in Solids by Auxiliary-Field Quantum Monte Carlo

技术总结该奖项支持一种新的量子蒙特卡罗方法的开发和应用的计算和理论研究以及教育，以回答特定于材料的问题，重点放在固体上，从强关联的过渡金属化合物到中等关联的原型半导体，再到基本模型，如电子气和多带类Hubbard模型。无相辅助场量子蒙特卡罗方法是由Pi和合作者发明的。最近的努力使其发展成为可用于电子结构计算的最精确的基态多体方法之一。PI将把这种方法应用于实体，并继续其发展。所涉及的问题包括固体中电子结构和激发态的计算，电子密度和离子作用力的计算，成对波函数的使用以及磁序和非常规相的计算。无相辅助场量子蒙特卡罗方法为关联系统晶格模型中的多体计算以及实际材料中的多体计算提供了一个新的通用框架。在并行机上实现的算法具有良好的可扩展性。这项研究旨在利用新的持续PB级计算设施进行突破性计算。PI将继续努力将研究与教育和推广活动相结合，促进计算科学教育和校园研究的跨学科合作，并通过暑期和冬季学校和工作坊在培养学生、博士后和高级研究人员方面发挥积极作用。非技术性总结该奖项支持计算和理论研究与教育，以增进我们对材料性质的了解，并提高我们在计算机上进行预测计算的能力。材料中有趣的效应和新的现象可以起源于电子运动中的复杂关联，这些关联是由电子之间的相互作用产生的。需要对材料中电子的量子力学描述来捕捉电子的错综复杂的芭蕾及其对材料性质的影响。该项目支持开发一种由PI开创的有前途的计算机模拟技术的研究，这种技术可以描述材料的组成电子并捕捉它们的相关运动。PI的方法为对材料和物质的新状态进行更准确和更具预测性的计算机模拟提供了新的机会。PI旨在推进具有强相互作用电子的材料的研究，建立准确的基准计算，并增强基于基本量子力学的材料模拟能力。所发展的理论框架和技术也可能对化学和物理学的其他分支产生影响。国际和平研究所将继续将研究与教育和外联活动相结合，方法是指导本科生和研究生进行研究，将该项目的材料纳入新课程，接触少数族裔学生，并在校园计算科学教育和研究方面促进跨学科合作。在更大的科学界，PI将通过学校和讲习班继续在培训学生、博士后和高级研究人员方面发挥积极作用，并开发用于实践学习新的理论和计算方法的软件和教程。

TECHNICAL SUMMARYThis award supports computational and theoretical research and education on the development and applications of a new quantum Monte Carlo method to answer materials-specific questions, focusing on solids ranging from strongly correlated transition metal compounds to moderately correlated prototypical semiconductors to fundamental models such as the electron gas and multi-band Hubbard-like models. The phaseless auxiliary-field quantum Monte Carlo method was invented by the PI and collaborators. Recent efforts have developed it into one of the most accurate ground-state many-body methods available for electronic structure calculations. The PI will apply this method to solids, and continue its development. Questions that will be addressed include electronic structure and excited states calculations in solids, computations of electronic density and ionic forces, the use of pairing wave functions and calculations of magnetic order and unconventional phases.The phaseless auxiliary-field quantum Monte Carlo method offers a new, general framework for many-body calculations in lattice models of correlated systems as well as in real materials. Implemented algorithms on parallel computers have excellent scalability. The research aims to take advantage of the new sustained petascale computing facilities to carry out breakthrough calculations.The PI will continue efforts to integrate research with education and outreach activities, foster interdisciplinary collaboration in computational science education and research on campus, and play an active role in training students, post-docs, and senior researchers through summer and winter schools, and workshops.NON-TECHNICAL SUMMARYThis award supports computational and theoretical research and education to advance our understanding of materials properties, and improve our ability to do predictive calculations on computers. Interesting effects and new phenomena in materials can originate from the complicated correlations in the motion of the electrons that result from the interactions among them. A quantum mechanical description of the electrons in a material is required to capture the intricate ballet of the electrons and its consequences for the properties of the material.This project supports research to develop a promising computer simulation technique pioneered by the PI, one that can describe materials down to the constituent electrons and capture their correlated motion. The PI's method offers new opportunities for more accurate and predictive computer simulations of materials and new states of matter. The PI aims to advance the study of materials with strongly interacting electrons, establish accurate benchmark calculations, and enhance the capabilities of materials simulation based on fundamental quantum mechanics. The theoretical framework and techniques developed may also have impact on chemistry and other branches of physics. The PI will continue to integrate research with education and outreach activities by mentoring both undergraduate and graduate students in research, incorporating materials from this project into new courses, reaching out to minority students, and fostering interdisciplinary collaboration in computational science education and research on campus. In the larger scientific community, the PI will continue to play an active role in training students, post-docs, and senior researchers through schools and workshops, and developing software and tutorials for hands-on learning of new theoretical and computational approaches.