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The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here, we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. By scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties.

Nanoporous materials are emerging as potential candidates for a wide range of technological applications in environment, electronic, and optoelectronics, to name just a few. Within this active research area, experimental works are predominant while theoretical/computational prediction and study of these materials face some intrinsic challenges, one of them is how to predict porous structures. We propose a computationally and technically feasible approach for predicting zinc oxide structures with hollows at the nano scale. The designed zinc oxide hollow structures are studied with computations using the density functional tight binding and conventional density functional theory methods, revealing a variety of promising mechanical and electronic properties, which can potentially find future realistic applications.

Inorganic clathrates with the type II crystal structure are of interest as potential materials for high temperature thermoelectric applications. In this study we present ab initio calculations for the electronic and phonon properties of several Sn type II clathrate compositions with partial Ga substitution on the framework, empty cage Sn136, and compounds of Sn136 filled with inert Xe atoms. It is found that cage disorder due to atomic substitution and guest encapsulation affect the fundamental characteristics of these materials in profound ways. We determine that the stability of these materials is enhanced by the presence of guests and lack of direct Ga Ga bonds in disordered clathrates. Inert Xe atoms provide a unique opportunity to preserve the overall electronic structure of Sn136 and take advantage of the loosely bound guest rattling for enhanced phonon scattering. The calculated energy bands and density of states, as well as phonon band structure and mode Gruneisen parameter, enable further analysis of type II Sn clathrates and reveal interesting structure-property relations.

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.

This article provides an overview of the present state-of-the-art pertaining to polymer capacitor dielectrics appropriate for high electrostatic energy density applications. The challenges and opportunities surrounding capacitor materials development/discovery in a practical context are reviewed. It is pointed out that several hierarchical considerations ranging from dielectric, electronic, morphological, processing, reliability and electrical characteristics need to be confronted and addressed adequately before one can progress from “standard” materials (such as biaxially oriented polypropylene) toward next generation materials. Nevertheless, it is argued that the prospects for systematic approaches toward polymer dielectrics discovery appear to be strong, especially given recent successes and evidence that a cooperative computation-synthesis-processing-characterization paradigm can bear fruit. It is hoped that this article will be particularly useful for a new researcher entering the field as it presents a snapshot of various critical aspects of this emerging field in one comprehensive narrative.

Two cage-like nanoporous polymorphs composed of spheroidal cages (ZnO)24 as building block in high symmetrical cubic lattice frameworks are proposed. By using this cluster, instead of atom, as primary building block we studied various scheme of its assembling via bottom up cluster-cluster coalescence schemes. First principles and density-functional tight binding (DFTB+) calculations confirm that the new polymorphs are energetically, dynamically, and mechanically stable. The two newly found nanoporous phases possess an intrinsic wide direct band gap preserving from wurtzite tetragonal-based bonding. The high cubic symmetry wide bandgap semiconductor and its derivatives are expected to have broad applications in photocatalysis, and biomedicine.

The fidelity of data is of paramount importance in the construction of reliable and accurate machine learning (ML) models. Low-fidelity data, although noisy, can usually be obtained for a large number of material samples. High-fidelity data, on the other hand, is time-consuming and oftentimes, only available for a limited number of target samples. While the former can provide useful information to help generalize the ML models over large materials space, the latter is useful to build more accurate surrogate models. Information fusion schemes that utilize the data available at multiple levels of fidelity can outperform traditional single fidelity based ML methods, such as Gaussian process regression. In this work, a variant of the multi-fidelity information fusion scheme, namely multi-fidelity co-kriging, is used to build powerful prediction models of polymer bandgaps. To benchmark this strategy, we utilize a bandgap dataset of 382 polymers, obtained at two levels of fidelity: using the Perdew-Burke-Ernzerhof (PBE) exchange-correlational functional (“low-fidelity”) and the Heyd-Scuseria-Ernzerhof (HSE06) functional(“high-fidelity”) of density functional theory. The multi-fidelity model, trained on both PBE and HSE06 data, outperforms a single-fidelity Gaussian process regression model trained on just HSE06 band-gaps in a number of scenarios and is also able to generalize better to a more diverse chemical space.