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X-ray Crystallography

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The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
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Related Experiment Video

Updated: May 23, 2025

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

LoreX: A Low-Energy Region Explorer Boosts Efficient Crystal Structure Prediction.

Chuan-Nan Li^1,2,3, Han-Pu Liang^3,4, Siyuan Xu⁵

¹Department of Physics, University of Science and Technology of China, Hefei 230026, China.

Journal of the American Chemical Society

|March 11, 2025

Summary

This summary is machine-generated.

A new method called LoreX uses graph deep learning to quickly find low-energy crystal structures. This accelerates materials design by efficiently exploring potential energy surfaces.

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Area of Science:

Materials Science
Computational Chemistry
Artificial Intelligence

Background:

Machine learning significantly advances crystal structure prediction (CSP) for materials design.
A major challenge in CSP is the slow identification of low-energy regions on the potential energy surface (PES).

Purpose of the Study:

To develop a novel method, LoreX, for rapid exploration of low-energy regions on the PES.
To enhance the overall efficiency of crystal structure prediction.

Main Methods:

Utilizing graph-deep-learning-based potential energy surface (PES) slicing.
Classifying structures into prototypes to divide and conquer the PES for efficient exploration.

Main Results:

LoreX accurately and efficiently locates low-energy regions on the PES using minimal samples (e.g., 100).
Validated on 27 diverse compounds, demonstrating its effectiveness.
Successfully applied to discover new boron allotropes and solve complex structures like CuIn5Se8.

Conclusions:

LoreX establishes a new paradigm for rapid PES exploration.
Offers a highly efficient and broadly applicable approach to accelerate CSP and materials discovery.