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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

Acceleration of scintillator discovery. Scintillator discoveries are defined as those which meet meet minimal requirements for light yield and decay time employing our QSPR, which also addresses issues in uncertainty. The prior discoveries in scintillator compounds are taken from reference [39] and the references therein. This demonstrates the applicability of our approach for significantly accelerating the discovery of new materials, while reducing the limitations and assumptions of other approaches. This work provides a generalized framework for developing large ‘virtual’ material libraries.
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Figure 8: Acceleration of scintillator discovery. Scintillator discoveries are defined as those which meet meet minimal requirements for light yield and decay time employing our QSPR, which also addresses issues in uncertainty. The prior discoveries in scintillator compounds are taken from reference [39] and the references therein. This demonstrates the applicability of our approach for significantly accelerating the discovery of new materials, while reducing the limitations and assumptions of other approaches. This work provides a generalized framework for developing large ‘virtual’ material libraries.

Mentions: Having developed a QSPR, we can then apply it to a ‘virtual’ library of compounds. While the compounds can theoretically contain any combination of elements, only those represented within the training data provide sufficiently high confidence in predictions. For example, if elements A, B and C are represented within the training data, then any compound containing a permutation of A, B and C may be included in the predictions. In this way, a significant number of compounds are rapidly calculated and those that have the target properties can be identified. Through the development of the QSPR which is based on descriptors capturing uncertainty and being physically relevant, we have significantly accelerated the process of data creation (figure 8).


Informatics derived materials databases for multifunctional properties
Acceleration of scintillator discovery. Scintillator discoveries are defined as those which meet meet minimal requirements for light yield and decay time employing our QSPR, which also addresses issues in uncertainty. The prior discoveries in scintillator compounds are taken from reference [39] and the references therein. This demonstrates the applicability of our approach for significantly accelerating the discovery of new materials, while reducing the limitations and assumptions of other approaches. This work provides a generalized framework for developing large ‘virtual’ material libraries.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Acceleration of scintillator discovery. Scintillator discoveries are defined as those which meet meet minimal requirements for light yield and decay time employing our QSPR, which also addresses issues in uncertainty. The prior discoveries in scintillator compounds are taken from reference [39] and the references therein. This demonstrates the applicability of our approach for significantly accelerating the discovery of new materials, while reducing the limitations and assumptions of other approaches. This work provides a generalized framework for developing large ‘virtual’ material libraries.
Mentions: Having developed a QSPR, we can then apply it to a ‘virtual’ library of compounds. While the compounds can theoretically contain any combination of elements, only those represented within the training data provide sufficiently high confidence in predictions. For example, if elements A, B and C are represented within the training data, then any compound containing a permutation of A, B and C may be included in the predictions. In this way, a significant number of compounds are rapidly calculated and those that have the target properties can be identified. Through the development of the QSPR which is based on descriptors capturing uncertainty and being physically relevant, we have significantly accelerated the process of data creation (figure 8).

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