Limits...
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

Subset B in the example of rough set approach for defining separation of light yield categories. Low light yield compounds are shown in red triangles, medium light yield compounds are green squares, purple diamonds are high light yield compounds, and the very high compounds are shown blue circles. In this figure, we show three cuts (shown as lines dividing the classes of material). Two of the cuts are for density and one cut is for Stoke’s shift. The accuracy of these cuts in discriminating the light yield categories is not sufficient, which indicates the need for further descriptors to be added.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Subset B in the example of rough set approach for defining separation of light yield categories. Low light yield compounds are shown in red triangles, medium light yield compounds are green squares, purple diamonds are high light yield compounds, and the very high compounds are shown blue circles. In this figure, we show three cuts (shown as lines dividing the classes of material). Two of the cuts are for density and one cut is for Stoke’s shift. The accuracy of these cuts in discriminating the light yield categories is not sufficient, which indicates the need for further descriptors to be added.

Mentions: Rough set builds on traditional set theory by defining a boundary region. Within the lower approximation region, a compound belongs to a specific class (in this case LY category), outside the outer approximation, the compound does not belong to that class. However, in the boundary region, defined as the space between the upper and lower approximations, a compound may possibly belong to the class. We first define an information system S as S = (U, A), where U are the components of the system, in this case scintillator chemistries, and A are the attributes of the system, in this case the different material descriptors and properties. We also define the subset of A, which we term as B. B therefore contains a portion of the attributes contained within A. In the example following, B = (density, EC factor). B for the scintillator case is shown in figure 2, where the categories of scintillators are discriminated. If we consider only these two descriptors, we can classify the LY classes with only four cuts. However, 28% of the compounds are misclassified in this case. We therefore need to include further parameters to improve the discrimination of the compounds. This process of parameter combinations and number of cuts is performed for every permutation to identify the best combination of accuracy of set approximation and quality of classification, which is based on an uncertainty metric.


Informatics derived materials databases for multifunctional properties
Subset B in the example of rough set approach for defining separation of light yield categories. Low light yield compounds are shown in red triangles, medium light yield compounds are green squares, purple diamonds are high light yield compounds, and the very high compounds are shown blue circles. In this figure, we show three cuts (shown as lines dividing the classes of material). Two of the cuts are for density and one cut is for Stoke’s shift. The accuracy of these cuts in discriminating the light yield categories is not sufficient, which indicates the need for further descriptors to be added.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Subset B in the example of rough set approach for defining separation of light yield categories. Low light yield compounds are shown in red triangles, medium light yield compounds are green squares, purple diamonds are high light yield compounds, and the very high compounds are shown blue circles. In this figure, we show three cuts (shown as lines dividing the classes of material). Two of the cuts are for density and one cut is for Stoke’s shift. The accuracy of these cuts in discriminating the light yield categories is not sufficient, which indicates the need for further descriptors to be added.
Mentions: Rough set builds on traditional set theory by defining a boundary region. Within the lower approximation region, a compound belongs to a specific class (in this case LY category), outside the outer approximation, the compound does not belong to that class. However, in the boundary region, defined as the space between the upper and lower approximations, a compound may possibly belong to the class. We first define an information system S as S = (U, A), where U are the components of the system, in this case scintillator chemistries, and A are the attributes of the system, in this case the different material descriptors and properties. We also define the subset of A, which we term as B. B therefore contains a portion of the attributes contained within A. In the example following, B = (density, EC factor). B for the scintillator case is shown in figure 2, where the categories of scintillators are discriminated. If we consider only these two descriptors, we can classify the LY classes with only four cuts. However, 28% of the compounds are misclassified in this case. We therefore need to include further parameters to improve the discrimination of the compounds. This process of parameter combinations and number of cuts is performed for every permutation to identify the best combination of accuracy of set approximation and quality of classification, which is based on an uncertainty metric.

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