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Title: 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:
ORCiD logo [1];  [1];  [1];  [1];  [2];  [1]
  1. Univ. of Connecticut, Storrs, CT (United States)
  2. 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}
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 113 works
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Figures / Tables:

Figure 1. Figure 1.: Scheme for preparing the dataset of polymers and related materials. USPEX and minima-hopping are two structure prediction methods that were used for generating a majority of the dataset.

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Accelerating materials property predictions using machine learning
journal, September 2013

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Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
journal, February 2016

  • Mannodi-Kanakkithodi, Arun; Pilania, Ghanshyam; Huan, Tran Doan
  • Scientific Reports, Vol. 6, Issue 1
  • DOI: 10.1038/srep20952

Minima hopping: An efficient search method for the global minimum of the potential energy surface of complex molecular systems
journal, June 2004

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Predicting polymeric crystal structures by evolutionary algorithms
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  • The Journal of Chemical Physics, Vol. 141, Issue 15
  • DOI: 10.1063/1.4897337

The structure of poly-2,5-benzoxazole (ABPBO) and poly-2,6-benzothiazole (ABPBT) fibers by X-ray diffraction
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  • DOI: 10.1080/00222348508248023

Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration
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The Order of the Molecular Chains in Isotactic Polypropylene Crystals
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Molecular and Crystal Structure of Hexafluoroacetone—Ethylene Alternating Copolymer
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  • Matsubayashi, Hajime; Chatani, Yozo; Tadokoro, Hiroyuki
  • Polymer Journal, Vol. 9, Issue 2
  • DOI: 10.1295/polymj.9.145

Accelerated materials property predictions and design using motif-based fingerprints
text, January 2015


Quantum chemistry structures and properties of 134 kilo molecules
text, January 2014


The Chemical Space Project
text, January 2015


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Soft Matter Informatics: Current Progress and Challenges
journal, November 2018

  • Peerless, James S.; Milliken, Nina J. B.; Oweida, Thomas J.
  • Advanced Theory and Simulations, Vol. 2, Issue 1
  • DOI: 10.1002/adts.201800129

Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
journal, June 2019


A hybrid organic-inorganic perovskite dataset
journal, May 2017

  • Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi
  • Scientific Data, Vol. 4, Issue 1
  • DOI: 10.1038/sdata.2017.57

Active-learning and materials design: the example of high glass transition temperature polymers
journal, June 2019

  • Kim, Chiho; Chandrasekaran, Anand; Jha, Anurag
  • MRS Communications, Vol. 9, Issue 3
  • DOI: 10.1557/mrc.2019.78

High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials
journal, January 2017

  • Petousis, Ioannis; Mrdjenovich, David; Ballouz, Eric
  • Scientific Data, Vol. 4, Issue 1
  • DOI: 10.1038/sdata.2016.134

Solving the electronic structure problem with machine learning
journal, February 2019

  • Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit
  • npj Computational Materials, Vol. 5, Issue 1
  • DOI: 10.1038/s41524-019-0162-7

Data from: A polymer dataset for accelerated property prediction and design
dataset, March 2016


Atomistic mechanisms for chemical defects formation in polyethylene
journal, December 2018

  • Chen, Lihua; Tran, Huan Doan; Ramprasad, Rampi
  • The Journal of Chemical Physics, Vol. 149, Issue 23
  • DOI: 10.1063/1.5063944

Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers
journal, September 2017


Layered structures of organic/inorganic hybrid halide perovskites
text, January 2015


Data from: A polymer dataset for accelerated property prediction and design
dataset, March 2016


Electronic Structure of Polyethylene: Role of Chemical, Morphological and Interfacial Complexity
journal, July 2017


High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials
journal, January 2017

  • Petousis, Ioannis; Mrdjenovich, David; Ballouz, Eric
  • Scientific Data, Vol. 4, Issue 1
  • DOI: 10.1038/sdata.2016.134

A hybrid organic-inorganic perovskite dataset
journal, May 2017

  • Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi
  • Scientific Data, Vol. 4, Issue 1
  • DOI: 10.1038/sdata.2017.57

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.