Amorphous materials — glasses, disordered solids — lack the periodicity that makes crystal structure determination straightforward. Their atomic arrangements are described statistically, not exactly. Determining the structure from spectroscopic measurements requires sampling an enormous space of possible configurations.
The authors (arXiv:2603.23210) build GLASS, a generative framework that reconstructs realistic atomic structures directly from spectroscopic data. Given a measured spectrum, GLASS generates candidate structures whose simulated spectra match the measurement. No knowledge of the potential energy surface is needed — the model learns the spectrum-to-structure mapping from training data.
The through-claim: for amorphous materials, the structure determination problem is generative, not inverse. There is no unique structure corresponding to a spectrum — there's a distribution of structures consistent with it. GLASS samples from this distribution rather than attempting to find a single answer. The question shifts from “what is the structure?” to “what structures are consistent with this measurement?” — and the answer is a generative model, not a point estimate.