标题： 量子材料中的多尺度光-物质动力学：从电子到拓扑超晶格 显示英文标题

标题： Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices

摘要： 光-物质在拓扑量子材料中的动力学使得超低功耗、超快速设备成为可能。 一个挑战是在具有更高异构性和低精度重点的艾克萨浮点运算计算机上，对光、电子和原子的多场和粒子方程进行跨越广阔时空尺度的模拟。 我们提出了一种范式转变，通过利用硬件异构性和低精度算术来解决多尺度/多物理场/异构性挑战。 分治重合算法将问题不仅分为空间子问题，还分为小动态范围和最小相互信息的物理子问题，这些子问题被映射到最佳特性匹配的硬件单元上，而元模型空间代数则最小化了通信和精度需求。 使用Aurora的60,000个GPU，DC-MESH（分治-征服麦克斯韦-埃伦费斯特表面跳跃）和XS-NNQMD（激发态神经网络量子分子动力学）模块的MLMD（多尺度光-物质动力学）软件比最先进的方法快152倍和3,780倍，针对1540万电子和1.23万亿原子的PbTiO3材料，前者实现了1.87 EFLOP/s的性能。 这使得首次研究了未来铁电“拓扑电子学”中拓扑超晶格的光诱导切换。 显示英文摘要

摘要： Light-matter dynamics in topological quantum materials enables ultralow-power, ultrafast devices. A challenge is simulating multiple field and particle equations for light, electrons, and atoms over vast spatiotemporal scales on Exaflop/s computers with increased heterogeneity and low-precision focus. We present a paradigm shift that solves the multiscale/multiphysics/heterogeneity challenge harnessing hardware heterogeneity and low-precision arithmetic. Divide-conquer-recombine algorithms divide the problem into not only spatial but also physical subproblems of small dynamic ranges and minimal mutual information, which are mapped onto best-characteristics-matching hardware units, while metamodel-space algebra minimizes communication and precision requirements. Using 60,000 GPUs of Aurora, DC-MESH (divide-and-conquer Maxwell-Ehrenfest-surface hopping) and XS-NNQMD (excited-state neural-network quantum molecular dynamics) modules of MLMD (multiscale light-matter dynamics) software were 152- and 3,780-times faster than the state-of-the-art for 15.4 million-electron and 1.23 trillion-atom PbTiO3 material, achieving 1.87 EFLOP/s for the former. This enabled the first study of light-induced switching of topological superlattices for future ferroelectric 'topotronics'.