MO-NanoDatabase: A metal-oxide nanostructured compound dataset composed of a huge number of metal-oxide nanocompounds, their global properties and 3D-structure

This paper presents the first important recompilation of metal-oxide nanocompounds, which is composed of three main parts: the first one includes several global properties of the nanocompounds whereas the second one includes their 3D structure, represented by the well-known XYZ format. Finally, the third part includes the structural nano QSAR named NanoFingerprint of these 3D structures. Modelling size-realistic metal-oxide nanomaterials to analyse some of their properties, such as chemical activity, solubility, or electronic structure, is a current challenge in computational and theoretical chemistry. Several nano QSAR models have been published based on global properties of these compounds, but few QSAR models also leverage their 3D structure. A general database of nanocompounds is crucial for the validation of current and future models. The global properties have been extracted from datasets published as the supporting material of papers that present new models for property prediction of metal-oxide nanocompounds [2-7]. The data has been curated, imposed the same units, formatted and given the same name per property since we realised the low generalisation on units, formats and nomenclature. Note the input parameters of the QSAR models and also the properties to be predicted have been put together as global properties in our database. Moreover, the 3D crystallographic structure has been computed through simulation computer applications of all the compounds since these structures could not be found in most of the cases. Since it is the first time that all this knowledge is compiled in a unique database, the purpose of MO-NanoDatabase is to be a reference database for prediction (chemical activity, solubility or electronic structure) of metal-oxide nanocompounds for current and future nano QSART models. Although many nanocompounds have been included, new versions of the database are not discarded if they bring substantial quantity of new nanocompounds presented in future papers.

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Publication Details

Journal
Data in Brief
Published
2025-03-22
DOI
https://doi.org/10.1016/j.dib.2025.111476
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

MO-NanoDatabase: A metal-oxide nanostructured compound dataset composed of a huge number of metal-oxide nanocompounds, their global properties and 3D-structure

Francesc Serratosa, Natàlia Segura-Alabart
1 citations
Data in Brief
Machine Learning in Materials Science
0.52
article

MO-NanoDatabase: A metal-oxide nanostructured compound dataset composed of a huge number of metal-oxide nanocompounds, their global properties and 3D-structure

Francesc Serratosa, Natàlia Segura-Alabart
article en
1 citations

Abstract

This paper presents the first important recompilation of metal-oxide nanocompounds, which is composed of three main parts: the first one includes several global properties of the nanocompounds whereas the second one includes their 3D structure, represented by the well-known XYZ format. Finally, the third part includes the structural nano QSAR named NanoFingerprint of these 3D structures. Modelling size-realistic metal-oxide nanomaterials to analyse some of their properties, such as chemical activity, solubility, or electronic structure, is a current challenge in computational and theoretical chemistry. Several nano QSAR models have been published based on global properties of these compounds, but few QSAR models also leverage their 3D structure. A general database of nanocompounds is crucial for the validation of current and future models. The global properties have been extracted from datasets published as the supporting material of papers that present new models for property prediction of metal-oxide nanocompounds [2-7]. The data has been curated, imposed the same units, formatted and given the same name per property since we realised the low generalisation on units, formats and nomenclature. Note the input parameters of the QSAR models and also the properties to be predicted have been put together as global properties in our database. Moreover, the 3D crystallographic structure has been computed through simulation computer applications of all the compounds since these structures could not be found in most of the cases. Since it is the first time that all this knowledge is compiled in a unique database, the purpose of MO-NanoDatabase is to be a reference database for prediction (chemical activity, solubility or electronic structure) of metal-oxide nanocompounds for current and future nano QSART models. Although many nanocompounds have been included, new versions of the database are not discarded if they bring substantial quantity of new nanocompounds presented in future papers.

Data in BriefVol. 60
Universitat Rovira i Virgili (ES)
Ministerio de Ciencia, Innovación y Universidades, Ministerio de Ciencia e Innovación, Agència de Gestió d'Ajuts Universitaris i de Recerca, European Regional Development Fund, Agencia Estatal de Investigación
Industry, innovation and infrastructure
Openalex Percentile: Top 41%
Machine Learning in Materials Science
0.52
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