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

加州理工学院的Garnet Chan获得了化学学部化学理论、模型和计算方法（CTMC）项目的奖励，开发了新的计算技术，使科学家能够解决生物系统的内部工作，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基本重新表述，这可能会使任何物理系统的量子力学在计算机上得到有效的模拟。外展活动的重点是招募代表性不足的本科生进行暑期研究，以期解决STEM学科的管道问题。更详细地说，在研究的第一部分中开发的计算技术侧重于扩展密度矩阵重整化群（DMRG）技术，以自信地处理在系统中发现的强相关电子结构，如氮酶的FeMo辅因子（FeMoCo）。这些技术包括改进的DMRG主动空间方法；时变摄动理论技术；以及随时间变化的光谱方法。在研究的第二部分，他正在进行一项密集的开发计划，将张量网络算法的全部通用性，跨越模型的门槛，引入从头计算量子化学。这项工作追求张量网络和现代机器学习算法之间的联系。

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.