Quantum Chemistry via General Tensor Networks

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奖励，以开发新的计算技术来解决量子力学方程。在具有过渡金属原子的分子的化学中可以发现这类难题，因为那里的电子在不同的原子间以协调一致的、相关的方式活动。陈博士的主要重点是开发解决这类问题的技术，特别是创建“稳健”的计算近似，因为它们可以应用于许多不同的化学问题，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，因此他更广泛工作的一个重点是为该领域生产新的教育材料，从多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的智力方向。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 kcal/mol的精度。第二是打造一条通向电子结构方法的道路，这种方法无论相互作用强度、符号结构或单或多参考性质如何，都能保持稳健。这是在实际化学问题中开展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等领域，将被浓缩成在线资源，这些资源将成为可用于培训下一代量子化学家的材料。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.