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Quantum Chemistry via General Tensor Networks

通过通用张量网络进行量子化学

基本信息

批准号：
2102505
负责人：
Garnet Chan
金额：
$ 46.5万
依托单位：
California Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102505&HistoricalAwards=false
关键词：
Quantum Chemistry via General Tensor

项目摘要

Garnet Chan of the California Institute of Technology is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop new computational techniques to solve the equations of quantum mechanics. Some difficult problems of this kind can be found in the chemistry of molecules with transition metal atoms, because the electrons there behave in a concerted, correlated manner across different atoms. Dr. Chan's main focus is to develop techniques to solve these classes of problems, and especially to create computational approximations that are “robust” in that they can be applied to many different problems in chemistry, without fear of them breaking down or providing an incorrect answer. His work draws on ideas from several disciplines, thus one focus of his broader work is to produce new educational material for the field that tackles different theoretical topics from the perspective of multiple disciplines. Garnet Chan is pursuing two specific complementary intellectual directions. The first is to develop quantum chemical techniques to treat dynamical correlation in very large multi-reference problems, in order to bring the most complex electronic structure problems within the reach of quantitative 1 kcal/mol accuracy. The second is to forge a path toward electronic structure methods that remain robust regardless of interaction strength, sign structure, or single- or multi-reference nature. This is based on foundational work to carry out three-dimensional tensor network simulations in realistic chemical problems. The ideas in the proposal, which draw on the areas of quantum chemistry, quantum information, and quantum circuits, will be condensed into online resources that will be material that can be used to train the next generation of quantum chemists.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.

加州理工学院的Garnet Chan获得了化学系化学理论、模型和计算方法项目的一个奖项的支持，以开发新的计算技术来求解量子力学方程。在含有过渡金属原子的分子的化学中，可以发现一些这类困难的问题，因为那里的电子在不同原子之间以协调的、相关的方式行为。陈博士的主要重点是开发解决这些问题的技术，特别是创建“稳健”的计算近似，因为它们可以应用于化学中的许多不同问题，而不必担心它们会崩溃或提供错误的答案。他的工作借鉴了多个学科的思想，因此他更广泛的工作的一个重点是为该领域制作新的教育材料，从多个学科的角度处理不同的理论主题。Garnet Chan正在追求两个特定的互补知识方向。首先是发展量子化学技术来处理非常大的多参考问题中的动力学相关性，以便使最复杂的电子结构问题达到定量1千卡/摩尔的精度。第二个是建立一条通往电子结构方法的道路，无论相互作用强度，符号结构或单或多参考性质如何，电子结构方法都保持稳健。这是基于在现实化学问题中进行三维张量网络模拟的基础工作。提案中的想法，借鉴了量子化学，量子信息和量子电路领域，将被浓缩成在线资源，将用于培养下一代量子化学家的材料。该奖项反映了NSF的法定使命，并被认为值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Low communication high performance ab initio density matrix renormalization group algorithms

DOI：
10.1063/5.0050902
发表时间：
2021-06-14
期刊：
JOURNAL OF CHEMICAL PHYSICS
影响因子：
4.4
作者：
Zhai, Huanchen;Chan, Garnet Kin-Lic
通讯作者：
Chan, Garnet Kin-Lic

Assessment of DFT functionals for a minimal nitrogenase [Fe(SH)4H]− model employing state-of-the-art ab initio methods

采用最先进的从头计算方法评估最小固氮酶 [Fe(SH)4H] 模型的 DFT 泛函

DOI：
10.1063/5.0152611
发表时间：
2023
期刊：
The Journal of Chemical Physics
影响因子：
0
作者：
Vysotskiy, Victor P.;Torbjörnsson, Magne;Jiang, Hao;Larsson, Ernst D.;Cao, Lili;Ryde, Ulf;Zhai, Huanchen;Lee, Seunghoon;Chan, Garnet Kin-Lic
通讯作者：
Chan, Garnet Kin-Lic

Systematic electronic structure in the cuprate parent state from quantum many-body simulations