A polymer dataset for accelerated property prediction and design

Huan, Tran Doan; Mannodi-Kanakkithodi, Arun; Kim, Chiho; Sharma, Vinit; Pilania, Ghanshyam; Ramprasad, Rampi

doi:10.1038/sdata.2016.12

Title: A polymer dataset for accelerated property prediction and design

Journal Article · Tue Mar 01 00:00:00 EST 2016 · Scientific Data

DOI:https://doi.org/10.1038/sdata.2016.12· OSTI ID:1248966

^[1]; Mannodi-Kanakkithodi, Arun ^[1]; Kim, Chiho ^[1]; Sharma, Vinit ^[1]; Pilania, Ghanshyam ^[2]; Ramprasad, Rampi ^[1]

Univ. of Connecticut, Storrs, CT (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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. As a result, it will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1248966

Report Number(s):: LA-UR-15-27665; sdata201612

Journal Information:: Scientific Data, Vol. 3; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 113 works

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