Mining Materials Design Rules from Data: The Example of Polymer Dielectrics

Mining of currently available and evolving materials databases to discover structure–chemistry–property relationships is critical to developing an accelerated materials design framework. The design of new and advanced polymeric dielectrics for capacitive energy storage has been hampered by the lack of sufficient data encompassing wide enough chemical spaces. Here, data mining and analysis techniques are applied on a recently presented computational data set of around 1100 organic polymers, organometallic polymers, and related molecular crystals, in order to obtain qualitative understanding of the origins of dielectric and electronic properties. By probing the relationships between crucial chemical and structural features of materials and their dielectric constant and band gap, design rules are devised for optimizing either property. Learning from this data set provides guidance to experiments and to future computations, as well as a way of expanding the pool of promising polymer candidates for dielectric applications

Mining Materials Design Rules from Data: The Example of Polymer Dielectrics

Mining of currently available and evolving materials databases to discover structure–chemistry–property relationships is critical to developing an accelerated materials design framework. The design of new and advanced polymeric dielectrics for capacitive energy storage has been hampered by the lack of sufficient data encompassing wide enough chemical spaces. Here, data mining and analysis techniques are applied on a recently presented computational data set of around 1100 organic polymers, organometallic polymers, and related molecular crystals, in order to obtain qualitative understanding of the origins of dielectric and electronic properties. By probing the relationships between crucial chemical and structural features of materials and their dielectric constant and band gap, design rules are devised for optimizing either property. Learning from this data set provides guidance to experiments and to future computations, as well as a way of expanding the pool of promising polymer candidates for dielectric applications

A polymer dataset for accelerated property prediction and design

This tarball includes 1073 CIF files, each of them provides the optimized structure and the accompanied properties calculated with first-principles computations. The README.txt file provides details on the inputs of the runs used to calculate the properties reportes

Rational Design of Organotin Polyesters

Large dielectric constant and band gap are essential for insulating materials used in applications such as capacitors, transistors and photovoltaics. Of the most common polymers utilized for these applications, polyvinyldiene fluoride (PVDF) offers a good balance between dielectric constant, >10, and band gap, 6 eV, but suffers from being a ferroelectric material. Herein, we investigate a series of aliphatic organotin polymers, p­[DMT­(CH₂)<i>_n</i>], to increase the dipolar and ionic part of the dielectric constant while maintaining a large band gap. We model these polymers by performing first-principles calculations based on density functional theory (DFT), to predict their structures, electronic and total dielectric constants and energy band gaps. The modeling and experimental values show strong correlation, in which the polymers exhibit both high dielectric constant, ≥5.3, and large band gap, ≥4.7 eV with one polymer displaying a dielectric constant of 6.6 and band gap of 6.7 eV. From our work, we can identify the ideal amount of tin loading within a polymer chain to optimize the material for specific applications. We also suggest that the recently developed modeling methods based on DFT are efficient in studying and designing new generations of polymeric dielectric materials