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 die

SUBMITTER: Huan TD 

PROVIDER: S-EPMC4772654 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature