Predicting Infrared Optical Properties of Materials Using Machine Learning Interatomic Potentials

Abstract

Infrared optical materials are critical for numerous applications, yet accurately characterizing their intrinsic optical properties remains challenging. Traditional theoretical approaches—ranging from empirical molecular dynamics to first-principles methods like density functional perturbation theory (DFPT)—face trade-offs between accuracy and computational cost, particularly for complex or low-symmetry material systems. Here, we tackle these challenges by introducing a fast and accurate infrared spectroscopy computational framework using machine learning interatomic potentials. By leveraging machine-learned interatomic forces, this method bypasses costly higher order DFPT calculations, enabling rapid extraction of phonon vibrational parameters. These parameters are then integrated into infrared-active vibration models to compute dielectric functions and infrared optical properties. Validated across diverse materials, our proposed framework demonstrates broad applicability while achieving a drastic reduction in computational cost compared to conventional methods. This framework bridges the gap between experimental characterization and theoretical predictions, offering a scalable tool for high-throughput screening and design of infrared optical materials.

Keywords

first principles calculations / infrared optical properties / lattice dynamic / machine learning interatomic potentials

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