SI2-SSE: Quantum Monte Carlo Software for a Broad Electronic Structure Research Community via Minimal Explicit Dependency (MED) programming

SI2-SSE：通过最小显式依赖 (MED) 编程为广泛的电子结构研究社区提供量子蒙特卡罗软件

• 批准号: 1534965
• 负责人: Cyrus Umrigar
• 金额: $ 42.08万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534965&HistoricalAwards=false
• 关键词: SI2; SSE; Quantum; Monte; Carlo

This project, jointly supported by the Advanced Cyberinfrastructure (ACI) Division, Division of Materials Research (DMR), and Division of Chemistry (CHE) at NSF, will provide a robust, usable software infrastructure for methods for materials analysis, which, when run on high-performance computers, have the potential to revolutionize and accelerate the discovery of materials for a variety of engineering applications ranging from better catalysts, to solar cells to thermoelectrics used for refrigeration. The big hurdle to doing this is that these methods for solving the fundamental equation of quantum mechanics, known as the Schroedinger equation, are either not sufficiently accurate or are too computationally expensive. The purpose of this proposal is to develop software for a class of stochastic techniques, called quantum Monte Carlo methods, that will allow scientists and engineers to solve the equation more accurately and thereby predict materials properties with greater accuracy. The software uses a new programming paradigm to make the use as well as the extension of the software by developers easier. If researchers find this programming paradigm useful, it may propagate to a much larger research community and may eventually become an integral part of a new programming language. Thus this project will contribute to the basic sciences and potentially contribute to the field of computer science as well.The goal of this proposal is to develop a software package consisting of a variety of quantum Monte Carlo (QMC) analysis tools. The software will have the capability to compute static and dynamic response properties, thereby greatly enhancing the range of applicability of QMC methods. Past research on optimizing many-body wave functions will aid in this goal since some of the formalism is the same. Both real space QMC methods, namely variational Monte Carlo (VMC) and diffusion Monte Carlo (DMC), and, determinant space QMC methods, namely semistochastic QMC (SQMC), which is an extension of the full configuration interaction QMC (FCIQMC) method, will be included, since each of these methods is the method of choice for some set of applications. The software uses the Minimal Explicit Dependency (MED) programming paradigm that greatly facilitates the development of complex programs. In particular, it lowers the barrier for researchers, who are not familiar with the inner workings of the program to contribute new functionality, thereby contributing to the long term survival of the software.

该项目由NSF的高级网络基础设施（ACI）部门，材料研究部门（DMR）和化学部门（CHE）联合支持，将为材料分析方法提供一个强大的，可用的软件基础设施，当在高性能计算机上运行时，有可能彻底改变和加速各种工程应用的材料发现，从更好的催化剂，到太阳能电池到用于制冷的热电器件。 要做到这一点，最大的障碍是这些求解量子力学基本方程（称为薛定谔方程）的方法要么不够精确，要么计算成本太高。 该提案的目的是为一类称为量子蒙特卡罗方法的随机技术开发软件，这将使科学家和工程师能够更准确地求解方程，从而更准确地预测材料特性。 该软件使用了一种新的编程模式，使开发人员更容易使用和扩展软件。 如果研究人员发现这种编程范式有用，它可能会传播到一个更大的研究社区，并最终成为一种新的编程语言的组成部分。因此，本项目将有助于基础科学，并可能有助于计算机科学领域以及本计划的目标是开发一个软件包，包括各种量子蒙特卡罗（QMC）分析工具。该软件将具有计算静态和动态响应特性的能力，从而大大提高QMC方法的适用范围。 过去对优化多体波函数的研究将有助于实现这一目标，因为一些形式是相同的。 这两个真实的空间QMC方法，即变分蒙特卡罗（VMC）和扩散蒙特卡罗（DMC），和，行列式空间QMC方法，即半随机QMC（SQMC），这是一个扩展的全组态相互作用QMC（FCIQMC）方法，将包括在内，因为这些方法中的每一个是一些应用程序的选择方法。 该软件使用最小显式依赖（MED）编程范式，极大地促进了复杂程序的开发。特别是，它降低了不熟悉程序内部工作的研究人员贡献新功能的障碍，从而有助于软件的长期生存。