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To the limits of the density matrix renormalization group in quantum chemistry, and beyond

量子化学中密度矩阵重整化群的极限及其他

基本信息

批准号：
1665333
负责人：
Garnet Chan
金额：
$ 45万
依托单位：
California Institute of Technology
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1665333&HistoricalAwards=false
关键词：
limits density matrix renormalization group

项目摘要

Garnet Chan at the California Institute of Technology is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) Program in the Chemistry Division to develop new computational techniques that will allow scientists to address the inner workings of the biological systems that help convert nitrogen in the air into the chemical forms that plants and animals use. In addition, he is pursuing a fundamental reformulation of quantum mechanics that potentially will enable the quantum mechanics of any physical system to be efficiently simulated on computers. The outreach activities focus on the recruitment of underrepresented undergraduates to pursue summer research with the view towards addressing the pipeline issue in the STEM disciplines.In more detail, the computational techniques being developed in the first part of the research focus on extending density matrix renormalization group (DMRG) techniques to confidently treat strongly correlated electronic structure as found in systems such as the FeMo cofactor (FeMoCo) of nitrogenase. These techniques include improved DMRG active space methods; time-dependent perturbation theory techniques; and time-dependent spectral methods. In the second part of the research he is pursuing an intensive development program to bring the full generality of tensor network algorithms, across the threshold of models, to ab initio quantum chemistry. This work pursues connections between tensor networks and modern machine learning algorithms.

加州理工学院的Garnet Chan获得了化学系化学理论、模型和计算方法（CTMC）项目的一项奖励，以开发新的计算技术，使科学家能够解决生物系统的内部工作原理，帮助将空气中的氮转化为植物和动物使用的化学形式。此外，他正在追求量子力学的根本性重新表述，这可能使任何物理系统的量子力学能够在计算机上有效地模拟。外联活动的重点是招募代表性不足的本科生进行暑期研究，以解决STEM学科的管道问题。在本研究的第一部分中，正在开发的计算技术的重点是扩展密度矩阵重整化群（DMRG）技术，以便有把握地处理在诸如铁钼辅因子（FeMoCo）之类的系统中发现的强关联电子结构固氮酶这些技术包括改进的DMRG主动空间方法;时变扰动理论技术;和时变谱方法。在研究的第二部分，他正在进行一项深入的开发计划，将张量网络算法的全面通用性，跨越模型的门槛，带到从头算量子化学中。这项工作追求张量网络和现代机器学习算法之间的联系。