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$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

Automated Identification of Molecular Crystals' Packing Motifs.

Donald Loveland¹, Bhavya Kailkhura², Piyush Karande³

¹Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.

Journal of Chemical Information and Modeling

|November 27, 2020

Summary

Autopack is a new automated framework that accurately labels molecular crystal packing motifs. This tool improves upon existing methods and enables large-scale studies of structure-property relationships in materials science.

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Area of Science:

Materials Science
Crystallography
Computational Chemistry

Background:

Molecular packing motifs are crucial for material properties but are difficult to label accurately.
Existing labeling algorithms are limited by manual processes and simplistic neighbor sampling.
Data sets of packing motifs are small and noisy, hindering research.

Purpose of the Study:

To develop an automated framework, Autopack, for accurate molecular crystal packing motif labeling.
To improve the efficiency and scale of packing motif analysis.
To enable large-scale investigations into structure-property relationships.

Main Methods:

An optimization algorithm that rotates crystal structures to identify representative molecules.
Automated framework (Autopack) for optimal crystal rotation and packing motif labeling.
Quantitative comparison of Autopack's performance against state-of-the-art methods.

Main Results:

Autopack demonstrates improved accuracy and performance over existing packing motif labeling techniques.
Provides the first quantitative benchmark for packing motif labeling algorithms.
Reveals complex relationships between chemical composition and packing motifs in large-scale studies.

Conclusions:

Autopack offers a robust solution for accurate and efficient packing motif analysis.
Facilitates the first large-scale exploration of composition-motif relationships.
Advances crystal engineering research by enabling new structure-property investigations.