Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models
This work addresses the challenge of materials discovery for researchers in materials science by bridging characterization and structure determination, though it appears incremental as it applies an existing generative method to a new domain.
The authors tackled the problem of predicting 3D atomic structures of disordered materials from target properties by introducing a diffusion model framework, demonstrating it on amorphous carbons using XANES spectra to reproduce key structural features and enable scale-agnostic generation from small datasets.
The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of amorphous carbons ($a$-C) as a representative material system from the target X-ray absorption near edge structure (XANES) spectra--a common experimental technique to probe atomic structures of materials. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e., with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.