PySlice: Routine Vibrational Electron Energy Loss Spectroscopy Prediction with Universal Interatomic Potentials

TL;DR

PySlice automates vibrational electron energy loss spectroscopy predictions from atomic structures using universal interatomic potentials, enabling routine, high-throughput phonon analysis and materials exploration in electron microscopy.

Contribution

Introduction of PySlice, a unified, automated framework combining machine learning interatomic potentials with TACAW for vibrational spectroscopy prediction from atomic structures.

Findings

Successfully predicts phonon dispersions and spectra for various materials.

Eliminates the need for specialized expertise in vibrational spectroscopy.

Supports high-throughput materials screening and data generation.

Abstract

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.