CMG COLLABORATIVE RESEARCH: Quantum Monte Carlo Calculations of Deep Earth Materials

CMG 合作研究：地球深部材料的量子蒙特卡罗计算

• 批准号: 1024936
• 负责人: David Ceperley
• 金额: $ 23万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1024936&HistoricalAwards=false
• 关键词: CMG; COLLABORATIVE; RESEARCH; Quantum; Monte

Quantum Monte Carlo is among the most precise simulation techniques to study realistic materials in physics and chemistry and provides a significant gain in precision compared with traditional density functional theory. One significant limitation of today?s QMC methods is the high computational demand. Since a substantial part of the QMC computation is spent in on forming and evaluating Slater determinants, the team plans to develop different localization transformations in order to obtain sparse determinants. The sparsity can be exploited in multilevel preconditioners, incomplete decomposition preconditioners, and iterative solvers to reach linear scaling with system size. The newly developed QMC methods will enable the team to obtain accurate equations of state, phase transitions, and elasticity of solid materials that are of high interest in geophysics. The spin state of iron in solid solutions magnesiowustite, perovskite and post-perovskite (Mg,Fe)SiO3 as well as the properties of water-carbon dioxide mixtures will be determined using QMC.Our understanding of the interior of the Earth comes from seismic observations and from the characterization of geological materials at high pressure. This characterization is not only obtained with high-pressure laboratory experiments but also with computer simulations because the properties of materials depend on the interactions between the atoms and those can be determined with computer simulations from the fundamental laws of physics. This project focuses on making those simulation methods much more accurate by developing new mathematical techniques to improve the quantum Monte Carlo method. These newly developed methods will enable the team to characterize different metal oxides, silicates, and mixtures of fluid water and carbon dioxide at high pressure.

量子蒙特卡洛（Monte Carlo）是研究物理和化学中逼真的材料的最精确的仿真技术之一，与传统密度功能理论相比，精确度可观。今天的QMC方法的一个重要局限性是高计算需求。由于QMC计算的很大一部分用于成立和评估Slater决定因素，因此团队计划开发不同的定位转换以获得稀疏的决定因素。可以在多级预处理，不完整的分解预处理和迭代求解器中利用稀疏性来达到系统尺寸的线性缩放。新开发的QMC方法将使团队能够获得在地球物理学中引起的固体材料的状态，相变和弹性的准确方程。固体溶液中铁的旋转状态镁层岩，钙钛矿和玻璃体后（mg，fe）Sio3以及水碳二氧化碳混合物的性能，将使用QMC确定。我们对地球内部的理解来自地震观测和高压的地质材料的特征。这种表征不仅是通过高压实验室实验获得的，而且还通过计算机模拟获得，因为材料的特性取决于原子之间的相互作用，而这些特征可以通过物理学基本定律来确定原子之间的相互作用。该项目着重于通过开发新的数学技术来改善量子蒙特卡洛方法来使这些仿真方法更加准确。这些新开发的方法将使团队能够在高压下表征不同的金属氧化物，硅酸盐和二氧化碳的混合物。