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• W2291908932 abstract "Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these approaches rely on identifying structure-property relationships by learning from a dataset of sufficiently large number of relevant materials. The learned information can then be used to predict the properties of materials not already in the dataset, thus accelerating the materials design. Herein, we develop a dataset of 1,073 polymers and related materials and make it available at http://khazana.uconn.edu/. This dataset is uniformly prepared using first-principles calculations with structures obtained either from other sources or by using structure search methods. Because the immediate target of this work is to assist the design of high dielectric constant polymers, it is initially designed to include the optimized structures, atomization energies, band gaps, and dielectric constants. It will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided." @default.
• W2291908932 created "2016-06-24" @default.
• W2291908932 creator A5017719372 @default.
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• W2291908932 creator A5075051974 @default.
• W2291908932 date "2016-03-01" @default.
• W2291908932 modified "2023-10-13" @default.
• W2291908932 title "A polymer dataset for accelerated property prediction and design" @default.
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• W2291908932 doi "https://doi.org/10.1038/sdata.2016.12" @default.
• W2291908932 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4772654" @default.
• W2291908932 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26927478" @default.
• W2291908932 hasPublicationYear "2016" @default.
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