Automation of Rietveld refinement through machine learning.

Abstract

Rietveld refinement is a widely used technique for determining crystal structures from powder X-ray diffraction data. Despite its broad applicability, the refinement process often requires substantial manual effort and expert knowledge, which limits the pace of materials research. Here, we introduce a methodology based on convolutional neural networks that enables automated Rietveld refinement. A systematically generated training dataset, which incorporates diverse structural and profile parameters, allows the network to capture complex pattern-structure relationships effectively. A refined crystal structure is directly extracted from the experimental powder X-ray diffraction pattern in a single inference step. The approach is validated using benchmark datasets of CeO₂, Tb₂BaCoO₅ and PbSO₄, achieving reliability factors comparable to those obtained from conventional methods. This work establishes a generalizable methodology by providing valuable insights into the development of autonomous diffraction analysis, with the potential to accelerate materials discovery and characterization.