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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

In order to screen the endless number of possible scintillator host lattice chemistries, we developed QSPRs for light yield and decay time as a function of the reduced descriptor set identified through the rough set analysis. The accuracy of these predictions allows us to rapidly screen a very large search space to identify those compounds with meet our design requirements of high light yield and fast decay time.
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Figure 7: In order to screen the endless number of possible scintillator host lattice chemistries, we developed QSPRs for light yield and decay time as a function of the reduced descriptor set identified through the rough set analysis. The accuracy of these predictions allows us to rapidly screen a very large search space to identify those compounds with meet our design requirements of high light yield and fast decay time.

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
In order to screen the endless number of possible scintillator host lattice chemistries, we developed QSPRs for light yield and decay time as a function of the reduced descriptor set identified through the rough set analysis. The accuracy of these predictions allows us to rapidly screen a very large search space to identify those compounds with meet our design requirements of high light yield and fast decay time.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: In order to screen the endless number of possible scintillator host lattice chemistries, we developed QSPRs for light yield and decay time as a function of the reduced descriptor set identified through the rough set analysis. The accuracy of these predictions allows us to rapidly screen a very large search space to identify those compounds with meet our design requirements of high light yield and fast decay time.
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