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Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

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Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures.

Hillary Pan¹, Alex M Ganose¹, Matthew Horton^1,2

¹Lawrence Berkeley National Laboratory, Energy Technologies Area, 1 Cyclotron Road, Berkeley, California 94720, United States.

Inorganic Chemistry

|January 8, 2021

Summary

This study introduces MaterialsCoord, a benchmark for crystal structures, and CrystalNN, a new algorithm for identifying near neighbors. CrystalNN performs comparably to existing methods in analyzing coordination environments for materials science.

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Area of Science:

Materials Science
Computational Chemistry
Crystallography

Background:

Coordination numbers and geometries are fundamental for predicting materials properties.
Automated algorithms for determining coordination environments are crucial for machine learning (ML) and structural analysis.

Purpose of the Study:

To introduce MaterialsCoord, a benchmark suite of experimentally derived crystal structures and their coordination environments.
To present CrystalNN, a novel algorithm for determining near neighbors in crystal structures.
To evaluate CrystalNN and other near-neighbor algorithms on the MaterialsCoord benchmark, assessing performance, computational cost, and sensitivity.

Main Methods:

Development of the MaterialsCoord benchmark suite with 56 experimentally derived crystal structures.
Implementation of the CrystalNN algorithm for near-neighbor identification.
Comparative analysis of CrystalNN against seven existing algorithms using the MaterialsCoord benchmark.
Assessment of computational demand and sensitivity to perturbations for each algorithm.
Investigation of bonding algorithm similarity on the Materials Project database.

Main Results:

CrystalNN demonstrated comparable performance to several established near-neighbor algorithms on the MaterialsCoord benchmark.
The study assessed the computational efficiency and robustness of various coordination determination algorithms.
Similarities and differences among bonding algorithms were analyzed when applied to a large materials database.

Conclusions:

The MaterialsCoord benchmark provides a valuable resource for evaluating coordination prediction algorithms.
CrystalNN is a viable new algorithm for determining near neighbors in crystal structures.
This work contributes to the advancement of algorithms for coordination prediction and structural descriptors in materials science and ML.