2003
DOI: 10.1557/proc-804-jj9.23
|View full text |Cite
|
Sign up to set email alerts
|

Combinatorial Materials Design through Database Science

Abstract: Large scale materials databases have been traditionally used for search and retrieval of experimental and theoretical data. In this paper, three different cases are used to illustrate applications of statistical techniques in databases that extend beyond searching. A complete large scale database of molten salts is visualized for pattern seeking. In the second case, a large virtual combinatorial library of chalcopyrite semiconductors is developed from a small experimental and theoretical dataset. This involves… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
7
0

Year Published

2006
2006
2023
2023

Publication Types

Select...
6
1

Relationship

3
4

Authors

Journals

citations
Cited by 14 publications
(7 citation statements)
references
References 2 publications
0
7
0
Order By: Relevance
“…Rajan and co-workers [111][112][113][114] applied data-mining methods, such as PCA, and predictive methods, such as partial least squares (PLS) to certain fields of materials science (zeolites, semiconductors, etc.). They connected conventional materials databases with experimental data sets in searches for correlations and patterns.…”
Section: Reviewsmentioning
confidence: 99%
“…Rajan and co-workers [111][112][113][114] applied data-mining methods, such as PCA, and predictive methods, such as partial least squares (PLS) to certain fields of materials science (zeolites, semiconductors, etc.). They connected conventional materials databases with experimental data sets in searches for correlations and patterns.…”
Section: Reviewsmentioning
confidence: 99%
“…Two extra descriptors, bond dissociation energy and bond length, were added to the previously-developed machine learning model for the predictions of chalcopyrite bandgaps. 57,58 The original subset of 15 features was reduced to 7 features using the sequential forward feature selection technique, and the prediction accuracy was improved by approximately 40%. Furthermore, the results show that the features associated with the last two elements of chalcopyrite are more relevant to bandgap prediction.…”
Section: Light Harvestingmentioning
confidence: 99%
“…It should be noted that data association has to be a deliberate and careful process of understanding which data was inputted and how to interpret from these patterns. Automatic methods of unsupervised data interpretation are possible and that is where data mining methods become very valuable27. As explained in the PCA methodology section, the percentages along each axis represent the variance of the data captured by each PC.…”
Section: Reduction Of Data Dimensionality Via Principal Component Anamentioning
confidence: 99%