AI 中文总结
本研究提出AES-Debye框架，通过校正分箱中心和稳健累积抑制误差，结合感知数据局部性的并行设计，实现了对9000万原子系统的高效准确德拜散射计算，可扩展至大型复杂材料体系。
AI 中文摘要
总散射模型对于表征纳米材料的结构和无序性至关重要。德拜散射方程（DSE）为弹性总散射提供了严格的计算途径，但其直接计算的计算量极大，因为必须在每个散射矢量处累积两两贡献，而基于分箱对距离分布或网格化快速傅里叶变换的常见加速策略会引入离散化和混叠伪影，损害漫散射的准确性。在此，我们提出AES-Debye，这是一种保留准确性的DSE框架，它使用校正后的分箱中心将对距离聚合成对分布函数（PDF），并采用数值稳健的累积方法来抑制离散化和求和误差。一种感知数据局部性的并行设计使其能在CPU和GPU上高效执行。我们通过在分布式内存CPU平台上用几分钟时间计算9000万个原子组成的系统（体积为0.1μm³）的高分辨率总散射曲线，展示了其强可扩展性。这些功能将准确的弹性总散射计算扩展到大型复杂系统，同时为后续结构分析提供高分辨率PDF。
英文摘要
Total scattering models are essential for characterizing the structure and disorder of nanoscale materials. The Debye scattering equation (DSE) provides a rigorous route to elastic total scattering, but its direct evaluation is computationally demanding because pairwise contributions must be accumulated at every scattering vector, whereas common acceleration strategies based on binned pair-distance distributions or gridded fast Fourier transforms can introduce discretization and aliasing artifacts that compromise diffuse-scattering accuracy. Here, we present AES-Debye, an accuracy-preserving DSE framework that aggregates pair distances into a pair distribution function (PDF) using corrected bin centers and numerically robust accumulation to suppress discretization and summation errors. A data-locality-aware parallel design enables efficient execution on CPUs and GPUs. We demonstrate strong scalability by computing a high-resolution total scattering profile for a system of 90 million atoms, $(0.1,μ\mathrm{m})^{3}$, in minutes on a distributed-memory CPU platform. These capabilities extend accurate elastic total scattering calculations to large, complex systems while simultaneously providing high-resolution PDFs for downstream structural analysis.
Comments24 pages, 10 figures
Journal refJ. Appl. Crystallogr. 59, 1478 (2026)
