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: 1025392
• 负责人: Ronald Cohen
• 金额: $ 19.82万
• 依托单位: Carnegie Institution of Washington
• 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 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025392&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.

量子蒙特卡罗是研究物理和化学中现实材料的最精确的模拟技术之一，与传统的密度泛函理论相比，它在精度上有显著的提高。今天的一个重要限制是什么？的QMC方法是高计算要求。由于QMC计算的很大一部分用于形成和评估斯莱特行列式，因此该团队计划开发不同的本地化变换以获得稀疏行列式。稀疏性可以在多级预处理器、不完全分解预处理器和迭代求解器中利用，以达到与系统大小的线性缩放。新开发的QMC方法将使团队能够获得精确的状态方程，相变和固体材料的弹性，这些都是物理学中非常感兴趣的。铁在固溶体镁橄榄石，钙钛矿和后钙钛矿（Mg，Fe）SiO 3以及水-二氧化碳混合物的性质的自旋状态将使用QMC。我们对地球内部的理解来自地震观测和高压下地质材料的表征。这种表征不仅可以通过高压实验室实验获得，还可以通过计算机模拟获得，因为材料的性质取决于原子之间的相互作用，并且可以根据物理学基本定律通过计算机模拟确定。该项目的重点是通过开发新的数学技术来改进量子蒙特卡罗方法，使这些模拟方法更加准确。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，硅酸盐以及流体水和二氧化碳的混合物。