A polymer dataset for accelerated property prediction and design
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. As a result, it will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.
- Authors:
-
- Univ. of Connecticut, Storrs, CT (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1248966
- Report Number(s):
- LA-UR-15-27665
Journal ID: ISSN 2052-4463; sdata201612
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Data
- Additional Journal Information:
- Journal Volume: 3; Journal ID: ISSN 2052-4463
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; electronic properties and materials; computational chemistry; density functional theory; atomistic models
Citation Formats
Huan, Tran Doan, Mannodi-Kanakkithodi, Arun, Kim, Chiho, Sharma, Vinit, Pilania, Ghanshyam, and Ramprasad, Rampi. A polymer dataset for accelerated property prediction and design. United States: N. p., 2016.
Web. doi:10.1038/sdata.2016.12.
Huan, Tran Doan, Mannodi-Kanakkithodi, Arun, Kim, Chiho, Sharma, Vinit, Pilania, Ghanshyam, & Ramprasad, Rampi. A polymer dataset for accelerated property prediction and design. United States. https://doi.org/10.1038/sdata.2016.12
Huan, Tran Doan, Mannodi-Kanakkithodi, Arun, Kim, Chiho, Sharma, Vinit, Pilania, Ghanshyam, and Ramprasad, Rampi. Tue .
"A polymer dataset for accelerated property prediction and design". United States. https://doi.org/10.1038/sdata.2016.12. https://www.osti.gov/servlets/purl/1248966.
@article{osti_1248966,
title = {A polymer dataset for accelerated property prediction and design},
author = {Huan, Tran Doan and Mannodi-Kanakkithodi, Arun and Kim, Chiho and Sharma, Vinit and Pilania, Ghanshyam and Ramprasad, Rampi},
abstractNote = {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.},
doi = {10.1038/sdata.2016.12},
journal = {Scientific Data},
number = ,
volume = 3,
place = {United States},
year = {Tue Mar 01 00:00:00 EST 2016},
month = {Tue Mar 01 00:00:00 EST 2016}
}
Web of Science
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High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials
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Solving the electronic structure problem with machine learning
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Data from: A polymer dataset for accelerated property prediction and design
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Electronic Structure of Polyethylene: Role of Chemical, Morphological and Interfacial Complexity
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High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials
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A hybrid organic-inorganic perovskite dataset
journal, May 2017
- Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi
- Scientific Data, Vol. 4, Issue 1
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