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The ability to rapidly develop materials with desired properties has atransformative impact on a broad range of emerging technologies. In this work,we introduce a new framework based on the diffusion model, a recent generativemachine learning method to predict 3D structures of disordered materials from atarget property. For demonstration, we apply the model to identify the atomicstructures of amorphous carbons ($a$-C) as a representative material systemfrom the target X-ray absorption near edge structure (XANES) spectra--a commonexperimental technique to probe atomic structures of materials. We show thatconditional generation guided by XANES spectra reproduces key features of thetarget structures. Furthermore, we show that our model can steer the generativeprocess to tailor atomic arrangements for a specific XANES spectrum. Finally,our generative model exhibits a remarkable scale-agnostic property, therebyenabling generation of realistic, large-scale structures through learning froma small-scale dataset (i.e., with small unit cells). Our work represents asignificant stride in bridging the gap between materials characterization andatomic structure determination; in addition, it can be leveraged for materialsdiscovery in exploring various material properties as targeted.

Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models