Spectroscopy-Guided Discovery of Three-Dimensional Structures of
  Disordered Materials with Diffusion Models
Hyuna Kwon Tim Hsu Wenyu Sun Wonseok Jeong Fikret Aydin James Chapman Xiao Chen Matthew R. Carbone Deyu Lu Fei Zhou Tuan Anh Pham
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.