skip to main content

This content will become publicly available on March 1, 2016

Title: A polymer dataset for accelerated property prediction and design

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:
OSTI Identifier:
1248966
Report Number(s):
LA-UR-15-27665
Journal ID: ISSN 2052-4463; sdata201612
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Scientific Data
Additional Journal Information:
Journal Volume: 3; Journal ID: ISSN 2052-4463
Publisher:
Nature Publishing Group
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; electronic properties and materials; computational chemistry; density functional theory; atomistic models