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Related Experiment Video

Updated: Sep 16, 2025

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

Published on: August 14, 2018

Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations.

Jaehoon Cha¹, Steven Tendyra^2,3, Alvin J Walisinghe^2,4

¹Scientific Computing, Rutherford Appleton Laboratory, Science and Technology Facilities Council, Harwell Science and Innovation Campus, Didcot, United Kingdom.

Communications Physics

|July 7, 2025

Summary

This study introduces a new machine learning method to control crystal growth and particle shape. It significantly reduces the time and computational cost of designing new crystalline materials with desired properties.

Keywords:

Chemical physics Coarse-grained models Computational methods

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Area of Science:

Materials Science
Crystallography
Computational Chemistry

Background:

Controlling crystal growth is crucial for material properties but current methods are costly and time-consuming.
Existing machine learning applications in crystal growth primarily focus on structure-property relationships, not morphological control.

Purpose of the Study:

To develop an efficient computational framework for controlling crystal morphology during growth.
To reduce the analytical and computational burden associated with crystal growth simulations.

Main Methods:

Utilized disentangling autoencoders combined with particle aspect ratio and spherical harmonics descriptors.
Developed a machine learning approach to analyze and predict crystal growth pathways.

Main Results:

Revealed continuous transformation pathways between different crystal morphologies.
Preserved underlying crystallographic principles during morphological transformations.
Significantly reduced data analytics burdens and design study timelines.

Conclusions:

The developed framework enhances simulation workflows for crystal growth.
Enables efficient exploration of crystal morphologies for targeted material design.
Facilitates the development of crystalline materials with specific functional properties.