PySlice: 基于通用原子间势能的振动电子能损谱预测
PySlice: Routine Vibrational Electron Energy Loss Spectroscopy Prediction with Universal Interatomic Potentials
AI总结：
PySlice通过整合通用机器学习原子间势能与TACAW方法，提供自动化框架实现振动电子能损谱预测，使声子物理研究和材料探索成为常规任务。
摘要：
电子显微镜中的振动光谱可以揭示具有纳米空间分辨率的声子激发，但常规预测仍然难以实现，因为需要碎片化的流程和专门的专家知识。在这里，我们介绍了PySlice，这是首个公开可用的Time Autocorrelation of Auxiliary Wavefunction（TACAW）方法的实现，提供了一个自动化框架，可以直接从原子结构生成动量和能量分辨的振动电子能损谱。通过将通用机器学习原子间势能与TACAW相结合，PySlice消除了每系统势能开发的瓶颈。用户输入原子结构，通过涵盖分子动力学、GPU加速电子散射和频域分析的统一流程，获得声子色散关系、谱衍射图样和谱图像。我们概述了代码背后的公式，展示了其在材料科学中的典型系统的应用，并讨论了其在高级分析和材料探索中的用途。模块化的Python架构还支持传统的电子显微镜模拟，提供了一个通用的平台用于成像和衍射计算。PySlice使振动光谱预测成为常规而非专门化，使计算筛选用于实验设计，系统地探索不同材料家族中的声子物理，以及高通量生成模拟数据用于未来机器学习模型的训练。
英文摘要：
Vibrational spectroscopy in the electron microscope can reveal phonon excitations with nanometer spatial resolution, yet routine prediction remains out of reach due to fragmented workflows requiring specialized expertise. Here we introduce PySlice, the first publicly available implementation of the Time Autocorrelation of Auxiliary Wavefunction (TACAW) method, providing an automated framework that produces momentum- and energy-resolved vibrational electron energy-loss spectra directly from atomic structures. By integrating universal machine learning interatomic potentials with TACAW, PySlice eliminates the bottleneck of per-system potential development. Users input atomic structures and obtain phonon dispersions, spectral diffraction patterns, and spectrum images through a unified workflow spanning molecular dynamics, GPU-accelerated electron scattering, and frequency-domain analysis. We outline the formulation behind the code, demonstrate its application to canonical systems in materials science, and discuss its use for advanced analysis and materials exploration. The modular Python architecture additionally supports conventional electron microscopy simulations, providing a general-purpose platform for imaging and diffraction calculations. PySlice makes vibrational spectroscopy prediction routine rather than specialized, enabling computational screening for experimental design, systematic exploration of phonon physics across materials families, and high-throughput generation of simulated data for training of future machine learning models.
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