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

技术概述：该奖项支持新的量子蒙特卡罗方法的开发和应用的计算和理论研究和教育，以回答材料特定问题，重点关注固体，从强相关过渡金属化合物到适度相关的原型半导体，再到基本模型，如电子气体和多波段哈伯德模型。无相辅助场量子蒙特卡罗方法是由PI及其合作者发明的。最近的努力已将其发展成为电子结构计算中最精确的基态多体方法之一。PI将把这种方法应用于固体，并继续发展。将解决的问题包括固体中的电子结构和激发态计算，电子密度和离子力的计算，配对波函数的使用以及磁有序和非常规相的计算。无相辅助场量子蒙特卡罗方法为相关体系的晶格模型和实际材料的多体计算提供了一个新的通用框架。在并行计算机上实现的算法具有很好的可扩展性。这项研究的目的是利用新的持续千万亿次计算设备来进行突破性的计算。PI将继续努力将研究与教育和推广活动结合起来，促进校园计算科学教育和研究的跨学科合作，并通过夏季和冬季学校和讲习班在培养学生，博士后和高级研究人员方面发挥积极作用。该奖项支持计算和理论研究和教育，以促进我们对材料特性的理解，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现象可能源于它们之间相互作用所产生的电子运动中的复杂关联。需要对材料中的电子进行量子力学描述，以捕捉电子的复杂芭蕾及其对材料性质的影响。该项目支持开发由PI首创的有前途的计算机模拟技术的研究，该技术可以描述材料的组成电子并捕获它们的相关运动。PI的方法为更精确和预测性的材料和物质新状态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，增强基于基础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。PI将继续通过指导本科生和研究生的研究，将本项目的材料纳入新课程，接触少数民族学生，以及促进校园计算科学教育和研究的跨学科合作，将研究与教育和外展活动结合起来。在更大的科学界，PI将继续发挥积极作用，通过学校和研讨会培训学生、博士后和高级研究人员，并开发软件和教程，以实际学习新的理论和计算方法。

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.