Computing Bulk Phase IR Spectra from Finite Cluster Data via Equivariant Neural Networks

Calculating accurate IR spectra from molecular dynamics simulations is crucial for understanding structural dynamics and benchmarking simulations. While machine learning has accelerated such calculations, leveraging finite-cluster data to compute condensed-phase IR spectra remains unexplored. In this work, we address a fundamental question: Can a machine learning model trained exclusively on electronic structure calculations of finite-size clusters reproduce the bulk IR spectrum? Using the atomic polar tensor as a target training property, we demonstrate that the corresponding equivariant neural network accurately recovers the bulk IR spectrum of liquid water, establishing the key link between finite-cluster data and bulk properties.