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Informatics derived materials databases for multifunctional properties

View Article: PubMed Central - PubMed

ABSTRACT

In this review, we provide an overview of the development of quantitative structure–property relationships incorporating the impact of data uncertainty from small, limited knowledge data sets from which we rapidly develop new and larger databases. Unlike traditional database development, this informatics based approach is concurrent with the identification and discovery of the key metrics controlling structure–property relationships; and even more importantly we are now in a position to build materials databases based on design ‘intent’ and not just design parameters. This permits for example to establish materials databases that can be used for targeted multifunctional properties and not just one characteristic at a time as is presently done. This review provides a summary of the computational logic of building such virtual databases and gives some examples in the field of complex inorganic solids for scintillator applications.

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Related in: MedlinePlus

The approach for defining relative importance of a descriptor on a property. The relative importance is defined as the number of cuts associated with a descriptor versus the total number of cuts. In this example, four cuts are shown, with one associated with density and three with electrochemical factor. We therefore identify electrochemical factor as approximately three times more important than density in this example.
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Figure 4: The approach for defining relative importance of a descriptor on a property. The relative importance is defined as the number of cuts associated with a descriptor versus the total number of cuts. In this example, four cuts are shown, with one associated with density and three with electrochemical factor. We therefore identify electrochemical factor as approximately three times more important than density in this example.

Mentions: Every combination of parameters and cuts is used and the accuracy and quality of the models are determined. The selection of model is then based on that which provides the best combination of these characteristics. From the final model selected, we identify the descriptors which are most important for the target property and the relative importance of that descriptor. The importance of the descriptor is defined as the number of cuts contributed by the descriptor versus total number of cuts. For example, density adds one cut in our example, while EC factor adds three cut (figure 4). In this example, density is therefore defined as having 25% relative importance on LY while EC factor has 75% relative importance. Further, the critical descriptor ranges are defined as the values where the lower approximations for a category occur. In this case, the critical values for density to achieve high LY are between six and eight, and EC factor from 1.25 to 1.75. When adding more parameters and thus cuts, these values can become smaller ranges. This defines then the measurement of the relative importance of a descriptor on a property and the critical design ranges for designing materials for target property.


Informatics derived materials databases for multifunctional properties
The approach for defining relative importance of a descriptor on a property. The relative importance is defined as the number of cuts associated with a descriptor versus the total number of cuts. In this example, four cuts are shown, with one associated with density and three with electrochemical factor. We therefore identify electrochemical factor as approximately three times more important than density in this example.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5036495&req=5

Figure 4: The approach for defining relative importance of a descriptor on a property. The relative importance is defined as the number of cuts associated with a descriptor versus the total number of cuts. In this example, four cuts are shown, with one associated with density and three with electrochemical factor. We therefore identify electrochemical factor as approximately three times more important than density in this example.
Mentions: Every combination of parameters and cuts is used and the accuracy and quality of the models are determined. The selection of model is then based on that which provides the best combination of these characteristics. From the final model selected, we identify the descriptors which are most important for the target property and the relative importance of that descriptor. The importance of the descriptor is defined as the number of cuts contributed by the descriptor versus total number of cuts. For example, density adds one cut in our example, while EC factor adds three cut (figure 4). In this example, density is therefore defined as having 25% relative importance on LY while EC factor has 75% relative importance. Further, the critical descriptor ranges are defined as the values where the lower approximations for a category occur. In this case, the critical values for density to achieve high LY are between six and eight, and EC factor from 1.25 to 1.75. When adding more parameters and thus cuts, these values can become smaller ranges. This defines then the measurement of the relative importance of a descriptor on a property and the critical design ranges for designing materials for target property.

View Article: PubMed Central - PubMed

ABSTRACT

In this review, we provide an overview of the development of quantitative structure–property relationships incorporating the impact of data uncertainty from small, limited knowledge data sets from which we rapidly develop new and larger databases. Unlike traditional database development, this informatics based approach is concurrent with the identification and discovery of the key metrics controlling structure–property relationships; and even more importantly we are now in a position to build materials databases based on design ‘intent’ and not just design parameters. This permits for example to establish materials databases that can be used for targeted multifunctional properties and not just one characteristic at a time as is presently done. This review provides a summary of the computational logic of building such virtual databases and gives some examples in the field of complex inorganic solids for scintillator applications.

No MeSH data available.


Related in: MedlinePlus