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CAREER: Local Correlation Approaches for High-Level Density Functional Theory Simulations of Large Systems

职业：大型系统高级密度泛函理论模拟的局部相关方法

基本信息

批准号：
1752769
负责人：
Chen Huang
金额：
$ 45.47万
依托单位：
Florida State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-15 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1752769&HistoricalAwards=false
关键词：
CAREER Local Correlation Approaches Level

项目摘要

Chen Huang of Florida State University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop new theoretical and computational methods for investigating the electronic properties of large, complex molecules and materials. The ability to model large systems at the electronic level is essential for the industrial and technological design of molecules and materials for clean energy, efficient batteries, and novel data storage devices. However, it is challenging to investigate large systems with the most accurate electronic structure methods, since computational cost grows exponentially with system size. Dr. Huang and his group are developing new quantum mechanical embedding methods for determining the electronic properties of large systems in a divide-and-conquer manner. The methods are focused on accurately describing the microscopic interactions among electrons---their quantum correlations---at scale. Finding new ways to address this "electron correlation problem" is regarded as one of the grand challenges of 21st century science. Dr. Huang's new algorithms and methods will be implemented in state-of-the-art, open source quantum chemistry codes and made available to the broader research community. Integrated with his research, Dr. Huang is training a diverse group of undergraduate and graduate students from engineering, chemistry, physics, and materials science to effectively utilize electronic structure methods, through a new problems-based density functional theory (DFT) course. The course lectures and YouTube tutorials are aimed at lowering the barrier for students to integrate DFT simulations within their research. Working with outreach programs at FSU, Dr. Huang is involving middle school and high school students in his research to increase their interest in pursuing STEM careers.The goal of this project is to develop new quantum mechanical embedding methods to obtain accurate reaction energies and electronic structures that scale to large chemical systems. The basic idea of embedding methods is to utilize a high-level electronic structure method within a region of interest, and to treat the rest of the system ("the environment") with a low-level, computationally-efficient method. Dr. Huang and his group are extending embedding methods in three new directions: (i) generalizing density-matrix embedding to metallic systems using a pseudopotential-like approach; (ii) extending the XCPP (exchange-correlation potential patching) methodology to construct RPA (random phase approximation) correlation energies and potentials in large systems in an atom-by-atom manner; and (iii) accelerating the convergence of RPA energy differences calculated with a stochastic sampling scheme by imposing appropriate moment constraints. The new techniques are being applied to challenging problems of significant fundamental and technological interest, including investigating the role of metallic copper in methanol synthesis; charge transfer and magnetic coupling at YBa2Cu3O7/La2/3Ca1/3MnO3 (superconducting/ferromagnetic) interface; and transport, storage, and release of oxygen in ceria for heterogeneous catalysis. The methodologies are being implemented as open source code in the ABINIT, Psi4, and CP2K quantum chemistry packages. The educational plan is focused on developing a problems-based DFT course including YouTube tutorials, to enable students to develop expertise in this widely-used tool of contemporary molecular and materials modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

佛罗里达州立大学的Chen Huang获得了化学系化学理论、模型和计算方法项目的奖励，以开发新的理论和计算方法来研究大型复杂分子和材料的电子性质。在电子水平上对大型系统进行建模的能力对于清洁能源、高效电池和新型数据存储设备的分子和材料的工业和技术设计至关重要。然而，它是具有挑战性的调查与最准确的电子结构方法的大型系统，因为计算成本与系统的大小呈指数增长。黄博士和他的团队正在开发新的量子力学嵌入方法，以分而治之的方式确定大型系统的电子特性。这些方法的重点是精确地描述电子之间的微观相互作用-它们的量子相关性-在尺度上。寻找新的方法来解决这个“电子相关问题”被认为是21世纪科学的重大挑战之一。黄博士的新算法和方法将在最先进的开源量子化学代码中实施，并向更广泛的研究界提供。结合他的研究，黄博士正在培训来自工程，化学，物理和材料科学的本科生和研究生，以有效地利用电子结构方法，通过一个新的基于问题的密度泛函理论（DFT）课程。课程讲座和YouTube教程旨在降低学生在研究中集成DFT模拟的障碍。黄博士与前南州立大学的外展项目合作，让初中和高中学生参与他的研究，以提高他们追求STEM职业的兴趣。该项目的目标是开发新的量子力学嵌入方法，以获得精确的反应能量和电子结构，可扩展到大型化学系统。嵌入方法的基本思想是在感兴趣的区域内利用高级电子结构方法，并使用低级计算有效的方法来处理系统的其余部分（“环境”）。Huang博士和他的团队正在三个新的方向上扩展嵌入方法：（i）使用伪势方法将密度矩阵嵌入推广到金属系统;（ii）扩展XCPP（交换相关电位修补）方法构建RPA（无规相位近似）以原子接原子的方式计算大系统中的关联能和势;以及（iii）通过施加适当的矩约束来加速利用随机采样方案计算的RPA能量差的收敛。新技术正在应用于具有重大基础和技术利益的挑战性问题，包括研究金属铜在甲醇合成中的作用;在YBa 2Cu 3 O 7/La 2/3Ca 1/3 MnO 3（超导/铁磁）界面的电荷转移和磁耦合;以及在氧化铈中用于多相催化的氧的运输、储存和释放。这些方法正在ABINIT、Psi 4和CP 2K量子化学包中作为开源代码实施。该教育计划的重点是开发一个基于问题的DFT课程，包括YouTube教程，使学生能够在这个广泛使用的当代分子和材料建模工具中发展专业知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。