Related Experiment Video
Updated: Aug 14, 2025

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
Published on: October 12, 2019
A Machine-Learning-Based Approach for Solving Atomic Structures of Nanomaterials Combining Pair Distribution
Magnus Kløve1, Sanna Sommer1, Bo B Iversen1
1Center for Integrated Materials Research, Department of Chemistry and iNano, Aarhus University, Aarhus, 8000, Denmark.
Abstract:
Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid-state chemistry and physics. Pair distribution function (PDF) analysis of X-ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases, a reliable structural motif is needed as a starting configuration for structure refinements. Here, an algorithm that is able to determine the crystal structure of an unknown compound by means of an on-the-fly trained machine learning model, which combines density functional theory calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape, is presented. Due to the nature of this landscape, even metastable configurations and stacking disorders can be identified.
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