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Title: Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem
Authors: Lach, Daniel
Zhdan, Uladzislau
Smoliński, Adam
Polański, Jarosław
Keywords: catalyst property prediction; catalysts informatics; infrastructure for catalyst property prediction; data sharing in catalyst discovery; data handling in catalyst discovery; cheminformatics for material discovery; catalytic material database; data science; data colletions
Issue Date: 2021
Citation: "International Journal of Molecular Sciences" (2021), Vol. 22, iss.10, art. no. 5176
Abstract: (1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine.
DOI: 10.3390/ijms22105176
ISSN: 1422-0067
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