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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.

No MeSH data available.


Related in: MedlinePlus

The data matrices for the PLS regressions, based on the reduced descriptor set from the rough set theory. By minimizing the descriptor base (X), we improve the modeling of the predictor variables (Y), which for our case includes light yield and decay time. PLS operates by performing PCA analyses on each matrix, and to then combine the matrices as defined in figure 5. This combination allows for co-linearity of variables to be defined so that over-fitting of properties is minimized.
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Figure 6: The data matrices for the PLS regressions, based on the reduced descriptor set from the rough set theory. By minimizing the descriptor base (X), we improve the modeling of the predictor variables (Y), which for our case includes light yield and decay time. PLS operates by performing PCA analyses on each matrix, and to then combine the matrices as defined in figure 5. This combination allows for co-linearity of variables to be defined so that over-fitting of properties is minimized.

Mentions: We have reduced the massive descriptor search space to five descriptors, which we use to predict two properties (LY and decay time). These five descriptors and two properties provide data matrices input into the PLS regression (figure 6). The combination of the different regressions thus provides models which are used to predict very rapidly the LY and decay time of new compounds. The accuracy of both regression models provides a significant acceleration which incorporates physics, uncertainty and empirical measurements (figure 7). This work then is used to accelerate the data calculation and provides a significant library for discovering scintillating materials.


Informatics derived materials databases for multifunctional properties
The data matrices for the PLS regressions, based on the reduced descriptor set from the rough set theory. By minimizing the descriptor base (X), we improve the modeling of the predictor variables (Y), which for our case includes light yield and decay time. PLS operates by performing PCA analyses on each matrix, and to then combine the matrices as defined in figure 5. This combination allows for co-linearity of variables to be defined so that over-fitting of properties is minimized.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: The data matrices for the PLS regressions, based on the reduced descriptor set from the rough set theory. By minimizing the descriptor base (X), we improve the modeling of the predictor variables (Y), which for our case includes light yield and decay time. PLS operates by performing PCA analyses on each matrix, and to then combine the matrices as defined in figure 5. This combination allows for co-linearity of variables to be defined so that over-fitting of properties is minimized.
Mentions: We have reduced the massive descriptor search space to five descriptors, which we use to predict two properties (LY and decay time). These five descriptors and two properties provide data matrices input into the PLS regression (figure 6). The combination of the different regressions thus provides models which are used to predict very rapidly the LY and decay time of new compounds. The accuracy of both regression models provides a significant acceleration which incorporates physics, uncertainty and empirical measurements (figure 7). This work then is used to accelerate the data calculation and provides a significant library for discovering scintillating materials.

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