Large-Scale Characterization of Chemical Bonding and Topology in the Materials Project Database

Topology is key to the determination of many physical and chemical properties of materials, such as electrical, optical, and magnetic properties, as well as thermal and mechanical behavior. However, despite the growing number of databases of crystalline materials available, there has been very little systematic effort to date to analyze their topology. In this work, we have leveraged recent algorithmic advances in the analysis of chemical bonding and topology determination in order to perform high-throughput analysis of topology of materials on a large-scale database of existing and hypothetical materials, the Materials Project data set of more than 170,000 structures. Beyond the statistical analysis of the most frequent topologies and coordination environments, the publication of these topological data will allow researchers to search for materials by topology and chemical environment, paving the way to enhanced performance in materials screening for applications. We demonstrated two examples of the usefulness of topological considerations in such computational screening.

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Publication Details

Journal
Chemistry of Materials
Published
2025-04-17
DOI
https://doi.org/10.1021/acs.chemmater.4c03172
Citations
5
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.59

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article

Large-Scale Characterization of Chemical Bonding and Topology in the Materials Project Database

François‐Xavier Coudert, Lionel Zoubritzky
5 citations
Chemistry of Materials
Machine Learning in Materials Science
2.59
article

Large-Scale Characterization of Chemical Bonding and Topology in the Materials Project Database

François‐Xavier Coudert, Lionel Zoubritzky
article en
5 citations

Abstract

Topology is key to the determination of many physical and chemical properties of materials, such as electrical, optical, and magnetic properties, as well as thermal and mechanical behavior. However, despite the growing number of databases of crystalline materials available, there has been very little systematic effort to date to analyze their topology. In this work, we have leveraged recent algorithmic advances in the analysis of chemical bonding and topology determination in order to perform high-throughput analysis of topology of materials on a large-scale database of existing and hypothetical materials, the Materials Project data set of more than 170,000 structures. Beyond the statistical analysis of the most frequent topologies and coordination environments, the publication of these topological data will allow researchers to search for materials by topology and chemical environment, paving the way to enhanced performance in materials screening for applications. We demonstrated two examples of the usefulness of topological considerations in such computational screening.

Chemistry of MaterialsVol. 37(9)
Centre National de la Recherche Scientifique (FR), Chimie ParisTech - PSL (FR), Université Paris Sciences et Lettres (FR), Institut de Recherche de Chimie Paris (FR), Air Liquide (France) (FR)
Agence Nationale de la Recherche, Association Nationale de la Recherche et de la Technologie, Grand Équipement National De Calcul Intensif
Openalex Percentile: Top 11%
Machine Learning in Materials Science
2.59
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