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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.


Standardized logic for developing virtual materials databases. This approach utilizes the entire material search space without a priori assumptions, identifies the minimum amount of information to describe the relevant problem, develop physics-driven design rules linking material and property, and then expansion of the materials knowledge base. In this review, we demonstrate the applicability of this approach to multiple material classes and problems.
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Figure 1: Standardized logic for developing virtual materials databases. This approach utilizes the entire material search space without a priori assumptions, identifies the minimum amount of information to describe the relevant problem, develop physics-driven design rules linking material and property, and then expansion of the materials knowledge base. In this review, we demonstrate the applicability of this approach to multiple material classes and problems.

Mentions: The approach for defining QSPRs, which we have previously utilized for accelerating data creation, leads to a discovery process far beyond iterative design. This approach has several factors which address challenges with traditional design. First, we consider the entire descriptor base, reducing any initial assumptions as to what is important in a materials property. Instead, this approach makes no assumptions as to what data is most important and instead the descriptors dictating properties are defined in a totally unbiased manner. Applying dimensionality reduction, the descriptor base is minimized to only those descriptors providing information that is both not redundant and also relevant to the properties of interest. The advantage of reducing the descriptor base is two-fold. First, the data measurement requirements are significantly reduced by minimizing the necessary amount of starting data. Second, by minimizing the data, the accuracy of the predictions increases by reducing the likelihood of over-fitting the data. This reduced descriptor base is then compared with property data with a least squares approach. In this case, we utilize partial least squares (PLS), which is similar to a multivariate regression while addressing co-linearity in the data, thereby reducing the over-fitting in the data and improving the accuracy of the models. The models, which we term as QSPRs, are in the form of equations with the target properties calculated as a linear combination of the reduced descriptor base. This equation can then be rapidly applied to new material and processing conditions, to significantly expand the material knowledge base, while being based on the governing physics and therefore relevant to development of virtual databases. This design strategy proposed as a standardized method for development of virtual materials databases is shown in figure 1. In this review, we utilize RST for defining the critical descriptors while considering uncertainty and PLS for building predictive models linking the reduced descriptor set with materials properties, while capturing the physics of the target properties and addressing the multi-collinearity in the data.


Informatics derived materials databases for multifunctional properties
Standardized logic for developing virtual materials databases. This approach utilizes the entire material search space without a priori assumptions, identifies the minimum amount of information to describe the relevant problem, develop physics-driven design rules linking material and property, and then expansion of the materials knowledge base. In this review, we demonstrate the applicability of this approach to multiple material classes and problems.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Standardized logic for developing virtual materials databases. This approach utilizes the entire material search space without a priori assumptions, identifies the minimum amount of information to describe the relevant problem, develop physics-driven design rules linking material and property, and then expansion of the materials knowledge base. In this review, we demonstrate the applicability of this approach to multiple material classes and problems.
Mentions: The approach for defining QSPRs, which we have previously utilized for accelerating data creation, leads to a discovery process far beyond iterative design. This approach has several factors which address challenges with traditional design. First, we consider the entire descriptor base, reducing any initial assumptions as to what is important in a materials property. Instead, this approach makes no assumptions as to what data is most important and instead the descriptors dictating properties are defined in a totally unbiased manner. Applying dimensionality reduction, the descriptor base is minimized to only those descriptors providing information that is both not redundant and also relevant to the properties of interest. The advantage of reducing the descriptor base is two-fold. First, the data measurement requirements are significantly reduced by minimizing the necessary amount of starting data. Second, by minimizing the data, the accuracy of the predictions increases by reducing the likelihood of over-fitting the data. This reduced descriptor base is then compared with property data with a least squares approach. In this case, we utilize partial least squares (PLS), which is similar to a multivariate regression while addressing co-linearity in the data, thereby reducing the over-fitting in the data and improving the accuracy of the models. The models, which we term as QSPRs, are in the form of equations with the target properties calculated as a linear combination of the reduced descriptor base. This equation can then be rapidly applied to new material and processing conditions, to significantly expand the material knowledge base, while being based on the governing physics and therefore relevant to development of virtual databases. This design strategy proposed as a standardized method for development of virtual materials databases is shown in figure 1. In this review, we utilize RST for defining the critical descriptors while considering uncertainty and PLS for building predictive models linking the reduced descriptor set with materials properties, while capturing the physics of the target properties and addressing the multi-collinearity in the data.

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.