Data from: 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. It will be progressively expanded by accumulating new materials
and including additional properties calculated for the optimized
structures provided.