Feature article Computational strategies for polymer dielectrics design C.C. Wang a, b , G. Pilania c , S.A. Boggs b , S. Kumar d , C. Breneman e , R. Ramprasad a, b, * a Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Storrs, CT 06269, USA b Institute of Materials Science, University of Connecticut, 97 North Eagleville Road, Storrs, CT 06269, USA c Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA d Department of Chemical Engineering, Columbia University, 500W. 120th St., New York, NY 10027, USA e Rensselaer Exploratory Center for Cheminformatics Research and Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA article info Article history: Received 23 November 2013 Received in revised form 25 December 2013 Accepted 29 December 2013 Available online xxx Keywords: Computation polymer dielectrics abstract The present contribution provides a perspective on the degree to which modern computational methods can be harnessed to guide the design of polymeric dielectrics. A variety of methods, including quantum mechanical ab initio methods, classical force-field based molecular dynamics simulations, and data- driven paradigms, such as quantitative structureeproperty relationship and machine learning schemes, are discussed. Strategies to explore, search and screen chemical and configurational spaces extensively are also proposed. Some examples of computation-guided synthesis and understanding of real polymer dielectrics are also provided, highlighting the anticipated increasing role of such compu- tational methods in the future design of polymer dielectrics. Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction Polymers offer a nearly infinite variety of material systems with diverse properties. Until recently, the formulation of polymers for specific applications was based on trial and error, guided by intu- ition. The purpose of the present contribution is to demonstrate the degree to which computational methods can guide the design of polymers, in the present case for dielectric applications [1e 12], which require high dielectric constant, large band gap, high dielectric strength, low dielectric loss, and appropriate glass tran- sition temperature and morphology. At the most fundamental level, computational quantum me- chanics, e.g., density functional theory (DFT), can be used to determine properties of dielectrics at the scale of a crystalline unit cell [13e17]. Such properties include structural and thermody- namic details, reasonable estimates of the band gap, electronic dielectric constant, ionic dielectric constant, and intrinsic break- down field [18e24]. In addition, impurity states in the band gap caused by common chemical impurities can be computed [25e28]. Realistic models can also be developed for metalepolymer in- terfaces in order to predict charge injection characteristics. Larger scale morphological features of polymers can be accessed practically at the present time only using molecular dynamics (MD) based on empirical interatomic potentials or force fields [29e33]. Such simulations can predict crystal structure, semicrystalline morphology and provide rough estimates of glass transition tem- perature and dielectric loss, although the latter is presently limited to loss in the GHz range [34e37]. The above methods can be classified as “physics-based”, as they are based on quantum mechanics, classical mechanics, and classical electromagnetism. An emerging class of methods, often referred to as “data-driven”, use various forms of multivariate analysis on experimental or computational data, based on complex variables with a physical relationship to the properties being predicted [38e 46]. Such systems are “trained” on available data and then used to predict properties of interest for polymers for which data are not available. An example of such data-driven approaches is quantita- tive structure property relationships (QSPR) [47e51], which can predict properties, such as glass transition temperature, melting temperature, etc., for which no fundamental approach is presently available. In this contribution, we provide a perspective on the application of modern computational approaches to the design of polymeric dielectrics. Section 2 addresses functionalization of a well- understood polymeric dielectrics, such as polyethylene (PE) and polypropylene (PP), to enhance its dielectric response. Section 3 discusses approaches to the discovery of entirely new classes of polymer dielectrics, both organic and organometallic. Strategies discussed include exploration of large chemical spaces and efficient computation of some relevant properties. The proper starting point * Corresponding author. Department of Materials Science and Engineering, Uni- versity of Connecticut, 97 North Eagleville Road, Storrs, CT 06269, USA. E-mail addresses: rampi@ims.uconn.edu, rampi@uconn.edu (R. Ramprasad). Contents lists available at ScienceDirect Polymer journal homepage: www.elsevier.com/locate/polymer 0032-3861/$ e see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.polymer.2013.12.069 Polymer xxx (2014) 1e10 Please cite this article in press as: Wang CC, et al., Computational strategies for polymer dielectrics design, Polymer (2014), http://dx.doi.org/ 10.1016/j.polymer.2013.12.069