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  1. Polymer informatics: Current status and critical next steps

    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 atmore » 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 in this paper, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.« less
  2. Thermal transport in phase-stabilized lithium zirconate phosphates

    The thermal properties of yttrium-stabilized lithium zirconate phosphate [LZP: Li1+x+yYxZr2-x(PO4)3 with x = 0.15, -0.2 ≤ y ≤ 0.4 and with x = 0.0, y = 0.0] are presented over a wide temperature range from 30 to 973 K, elucidating the interplay between structural phase transformations and thermal properties in a solid state superionic conducting material. At room temperature, the thermal conductivity decreases by more than 75% as the stoichiometry is changed from lithium deficient to excess and increases with increasing temperature, indicative of defect-mediated transport in the spark plasma sintered materials. The phase transformations and their stabilities are examinedmore » by x-ray diffraction and differential scanning calorimetry and indicate that the Y3+ substitution of Zr4+ is effective in stabilizing the ionically conductive rhombohedral phase over the entire temperature range measured, the mechanism of which is found through ab initio theoretical calculations. Finally, these insights into thermal transport of LZP superionic conductors are valuable as they may be generally applicable for predicting material stability and thermal management in the ceramic electrolyte of future all-solid-state-battery devices.« less
  3. Cage disorder and gas encapsulation as routes to tailor properties of inorganic clathrates

    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 ofmore » 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.« less
  4. 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 targetmore » 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. As a result, it will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.« less
  5. Machine-learning predictions of polymer properties with Polymer Genome


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