CDS&E: Ab Initio Ultrafast Dynamics of Spin, Valley and Charge in Quantum Materials

本基金由材料研究部的凝聚态物质与材料理论项目和化学部的化学理论、模型与计算方法项目资助。量子计算机有望执行任何当前或可想象的非量子计算机无法实现的计算，这使其成为国家最高研究重点之一。该奖项支持量子材料中电子运动的计算研究和教育。最近发现的几种材料显示出在单个电子中存储量子信息的潜力，这可能是下一代量子计算机和量子通信的关键。要实现这些材料的全部潜力，需要精确理解量子信息可以在电子自旋中存储多长时间，以及它最终如何通过与材料中原子的振动相互作用而消失。研究人员将开发一种计算方法，在大型超级计算机上模拟量子电子运动。他们将使用这项技术来预测电子自旋如何随飞秒到微秒的时间变化，在几种有前途的材料中，如卤化铅钙钛矿，包含重原子的自旋与电子的运动耦合。过渡金属二硫族化合物中的电子是存储量子信息的另一种选择，可以在多个所谓的“谷”中找到；研究人员还将研究电子谷和电子自旋如何耦合。对于每一种材料，他们将用极短的激光脉冲模拟这些量子态的相互作用，以解释自旋和谷动力学的实验测量结果。该奖项还将支持该团队在提高女性在STEM学科，特别是物理科学领域的参与度和代表性方面所做的努力。通过使用增强现实将模拟集成到直观的可视化中，他们将使本科生和高中生能够理解电子动力学。最后，这个项目将加强UCSC的研究基础设施，这是一个西班牙裔服务机构。本研究项目的目标是完全从第一性原理定量预测具有自旋、谷或其他内部自由度的电子的量子动力学。该研究团队将开发一种新的计算方法和相关的大规模并行开源软件，通过从头开始处理电子-电子、电子-声子和电子-光子相互作用，以林德布拉迪亚公式快速演化量子材料的密度矩阵。这将有助于计算自旋或谷极化的相干动力学和消相，以及它们在超快光谱中的实验特征。利用这种技术，他们将研究具有强自旋轨道耦合和Rashba分裂的系统（如卤化铅钙钛矿和铁电氧化物）中的自旋动力学，以及层状过渡金属二硫族化物中的谷动力学。这种全新的预测能力将促进超快光学和自由电子激光测量的定量分析，并精确预测量子材料的自旋弛豫。这对于自旋电子学、谷电子学和量子信息的新材料平台的设计和发现至关重要。这项提议的工作将在开源软件中武装材料研究界的第一原理量子动力学方法。这些将包括跟踪不同级别相干性的方法层次，相应的计算要求从小型计算机集群到未来的百亿亿次超级计算机。因此，它将提供预测量子材料中相干和非相干超快动力学所需的关键计算技术，大大超出了现有第一原理方法的能力。该项目资助的工作通过促进量子信息科学关键的自旋松弛和载流子动力学的定量模拟，直接响应了美国国家科学基金会的十大理念之一——量子飞跃。与该项目相关的教育活动旨在提高女性在STEM学科，特别是物理科学领域的参与度和代表性。它将通过增强现实平台将材料模拟的范围扩展到K-12教育。该项目还将加强加州大学圣地亚哥分校的研究基础设施，这是一所西班牙裔服务机构。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。

This grant is being funded by the Condensed-Matter and Materials Theory program in the Division of Materials Research and by the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry.Nontechnical SummaryThe promise of quantum computers to perform calculations beyond the reach of any current or conceivable non-quantum computer has made them one of the nation's highest research priorities. This award supports computational research and education on the motion of electrons in quantum materials. Several recently-discovered materials exhibit the potential to store quantum information in individual electrons that may hold the key to the next generation of quantum computers and quantum communication. Realizing the full potential of these materials requires precise understanding of how long quantum information can be stored in electron spins and how it disappears eventually by interacting with the vibrations of atoms in the material.The investigators will develop a computational methodology to simulate quantum electron motion on large supercomputers. They will use this technique to predict how electron spin changes over times ranging from femtoseconds to microseconds in several promising materials, such as lead halide perovskites, containing heavy atoms that couple spin to the movement of electrons. Electrons in transition-metal dichalcogenides, another alternative for storing quantum information, can be found in multiple so-called "valleys;" the investigators will also study how electron valley and electron spin couple. For each of these materials, they will simulate the interaction of these quantum states with extremely short laser pulses to interpret experimental measurements of spin and valley dynamics.This award will also support the team's effort in increasing participation and representation of women in STEM disciplines, especially in the physical sciences. By integrating simulations into intuitive visualizations using augmented reality, they will make electron dynamics understandable to undergraduate and high school students. Finally, this project will strengthen the research infrastructure at UCSC, a Hispanic Serving Institution.Technical summaryThe goal of this research project is to predict quantitatively quantum dynamics of electrons with spin, valley, or other internal degrees of freedom, entirely from first principles. The research team will develop a novel computational methodology and associated massively-parallel open-source software rapidly to evolve density matrices of quantum materials in a Lindbladian formulation, with ab initio treatment of electron-electron, electron-phonon, and electron-photon interactions. This will facilitate calculation of both coherent dynamics and dephasing of spin or valley polarization, along with their experimental signatures in ultrafast spectroscopy. Using this technique, they will investigate spin dynamics in systems with strong spin-orbit coupling and Rashba splitting such as lead halide perovskites and ferroelectric oxides, and valley dynamics in layered transition metal dichalcogenides. This fundamentally new predictive capability will facilitate quantitative analysis of ultrafast optical and free-electron laser measurements with linear and circular polarization, and accurate predictions of spin relaxation of quantum materials. This will be critical for the design and discovery of new material platforms for spintronics, valleytronics and quantum information.The proposed work will arm the materials research community with first-principles quantum dynamics methods in open-source software. These will include a hierarchy of methods that keep track of different levels of coherence, with corresponding computational requirements ranging from a small computer cluster to future exascale supercomputers. It will thereby deliver a key computational technique necessary for predicting coherent and incoherent ultrafast dynamics in quantum materials, extending significantly beyond the capabilities of existing first-principles methods. The work funded in this project responds directly to one of NSF's 10 Big Ideas, the Quantum Leap, by facilitating quantitative simulation of spin relaxation and carrier dynamics critical for quantum information science. The educational activities associated with this project aim to increase participation and representation of women in STEM disciplines, especially in the physical sciences. It will expand the reach of materials simulations to K-12 education through the platform of augmented reality. This project will also strengthen the research infrastructure at UCSC, a Hispanic Serving Institution.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.