发表机构
Argonne National Laboratory; The University of Chicago; Flatiron Institute; University of Modena and Reggio Emilia（阿贡国家实验室; 芝加哥大学; 平顶山研究所; 摩德纳和雷焦艾米利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍用于大规模激发态材料模拟的开源代码WEST，它在通用框架内实现多种理论，结合多种技术实现良好计算缩放，支持多种计算，通过并行化和GPU加速可模拟复杂系统，有代表性应用，是可扩展的相关模拟平台。
AI 中文摘要
我们展示了WEST，一个用于大规模激发态材料模拟的开源平面波赝势代码，并描述了其理论基础、软件架构和功能。WEST在一个通用算法框架内实现了全频GW、量子缺陷嵌入理论、贝叶斯-萨尔皮特方程和含时密度泛函理论，避免了虚拟电子态的显式计算。通过结合密度泛函和密度矩阵微扰理论、介电屏蔽和精确交换的低秩表示以及定位技术，WEST实现了与系统大小相关的良好计算缩放。该代码支持计算准粒子和中性激发能、光学和光致发光光谱、激发态势和非绝热耦合，具有可互操作的工作流程，可连接到量子化学、振动耦合和量子计算包。分层并行化策略和GPU加速实现了近乎理想的强缩放至数千个GPU，能够对超过一千个原子的系统进行精确的激发态模拟。代表性应用展示了该代码在不同材料类别中的准确性和通用性。WEST中实现的功能使其成为一个可扩展的平台，用于预测性激发态模拟、高通量材料发现以及为计算材料科学中的机器学习生成高保真数据集。
英文摘要
We present WEST, an open-source plane-wave pseudopotential code for large-scale excited-state materials simulations, and describe its theoretical foundations, software architecture, and capabilities. WEST implements full-frequency GW, quantum defect embedding theory, the Bethe-Salpeter equation, and time-dependent density functional theory within a common algorithmic framework that avoids the explicit computation of virtual electronic states. By combining density functional and density matrix perturbation theory, low-rank representations of the dielectric screening and exact exchange, and localization techniques, WEST achieves favorable computational scaling with system size. The code supports the calculation of quasi-particle and neutral excitation energies, optical and photoluminescence spectra, excited-state forces, and non-adiabatic couplings, with interoperable workflows connecting to quantum chemistry, vibronic coupling, and quantum computing packages. A hierarchical parallelization strategy and GPU acceleration deliver near-ideal strong scaling to thousands of GPUs, enabling accurate excited-state simulations of systems with more than a thousand atoms. Representative applications, spanning the full optical cycle of solid-state spin defects, self-trapped excitons in metal-halide perovskites, and the optical response of liquid water and ice, demonstrate the accuracy and versatility of the code across diverse material classes. The capabilities implemented in WEST establish the code as a scalable platform for predictive excited-state simulations, high-throughput materials discovery, and the generation of high-fidelity datasets for machine learning in computational materials science.
