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Related Experiment Video

Updated: Dec 28, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

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Published on: July 19, 2024

Sparse modeling of chemical bonding in binary compounds.

Yosuke Kanda¹, Hitoshi Fujii², Tamio Oguchi^1,2

¹Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan.

Science and Technology of Advanced Materials

|February 22, 2020

Summary

This study develops a simple model to predict crystal structure energy differences using atomic radii. The findings offer insights into chemical bonding and material structure prediction.

Keywords:

404 Materials informatics / Genomics Sparse modeling binary compounds chemical bonding machine learning

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Area of Science:

Materials Science
Computational Chemistry
Solid-State Physics

Background:

Understanding the energy differences between various crystal structures (e.g., zinc-blende and rock-salt) is crucial for predicting material properties.
Previous work established a foundation for developing simplified models based on material descriptors.

Purpose of the Study:

To construct a sparse model for quantifying the energy difference between zinc-blende and rock-salt structures in elemental and binary octet materials.
To identify the minimal set of descriptors required for accurate energy difference prediction.

Main Methods:

Utilized the linearly independent descriptor-generation method and exhaustive search, building upon prior research.
Developed a simplified model focusing on fundamental atomic properties.

Main Results:

The simplest effective model incorporates only the atomic radius of the constituent atoms.
This model successfully quantifies the energy difference between the specified crystal structures.

Conclusions:

Atomic radius is a key descriptor for predicting crystal structure energy differences in these material classes.
The model's physical meaning aligns with established chemical bonding concepts, specifically van Arkel-Ketelaar's triangle.