Learning materials spectra across modalities and scales with structure-conditioned neural fields

Abstract

Spectroscopic observables encode rich information about material behavior across diverse, often high-dimensional domains of energy, momentum, and other experimental coordinates, providing critical insights into the physical properties underlying material functionalities and applications. However, learning these responses directly from atomic structures remains challenging due to the complexity of both structural configurations and spectroscopic observation spaces. Here, we develop a structure-conditioned neural-field framework that learns continuous spectroscopic response functions by coupling an equivariant structural encoder with a coordinate-conditioned network. The framework accurately predicts spectroscopic observables across diverse observation domains, ranging from one-dimensional densities of states to multidimensional scattering functions. We show that fine-tuning with limited spectral supervision captures strain- and composition-dependent spectral changes, with validated transfer to alloy configurations containing hundreds of atoms and computational scalability demonstrated for configurations exceeding $10^4$ atoms. Our work establishes a versatile and computationally scalable approach to learning structure-spectroscopy relationships, providing a foundation for spectroscopic analysis and future materials characterization, inverse problems, and experimental design.