Rational Co‐Design of Polymer Dielectrics for Energy Storage

Abstract

Although traditional materials discovery has historically benefited from intuition-driven experimental approaches and serendipity, computational strategies have risen in prominence and proven to be a powerful complement to experiments in the modern materials research environment. It is illustrated here how one may harness a rational co-design approach-involving synergies between high-throughput computational screening and experimental synthesis and testing-with the example of polymer dielectrics design for electrostatic energy storage applications. Recent co-design efforts that can potentially enable going beyond present-day "standard" polymer dielectrics (such as biaxially oriented polypropylene) are highlighted. These efforts have led to the identification of several new organic polymer dielectrics within known generic polymer subclasses (e.g., polyurea, polythiourea, polyimide), and the recognition of the untapped potential inherent in entirely new and unanticipated chemical subspaces offered by organometallic polymers. The challenges that remain and the need for additional methodological developments necessary to further strengthen the co-design concept are then presented.

A hybrid organic-inorganic perovskite dataset

“…A limit of the form $\mathit{є}\mathrm{elec}~\mathrm{1/}{E}_{\mathrm{normalg}}^{\mathrm{normalHSE06}}$ shown in Fig. 5(d) has also been demonstrated for other classes of materials in the literature 13,35,36,51–62 .…”

“…Moreover, the introduction of an organic cation A into the perovskite structure can give raise of many different structural motifs 6,10–12 , making the class of halide-based HOIPs highly diverse. Rapidly and thoroughly screening this un-explored domain of the chemical space, for instance, with the emerging data-driven approaches 13–25 , may reveal new promising compounds potentially meeting the pressing need for lead-free perovskite solar cell materials 26 .…”

Electronic Structure of Polyethylene: Role of Chemical, Morphological and Interfacial Complexity

“…Polymers are widely used in electric and electronic devices, e.g., capacitors 1–8 , transistors 9, 10 , fuel cell membranes 11, 12 and high-voltage cables 13, 14 . The insulating behavior of polymers—or any material for that matter—becomes progressively (and in many cases, irreversibly) degraded over time, especially when they are exposed to heat, light, oxygen, moisture, mechanical stress, and the high electric fields encountered during operation 14–17 .…”

“…Typically, polymer degradation involves a wide variety of physical and chemical processes, spanning over several length and time scales. The highly complicated and coupled nature of these processes render detailed mechanistic studies far from being tractable, both computationally 18–24 and experimentally 14, 17, 24–28 , despite extensive recent efforts aimed at the rational design of polymer dielectrics 3, 6, 8, 29–32 .…”

A universal strategy for the creation of machine learning-based atomistic force fields

“…[1][2][3] Nevertheless, DFT-based MD simulations are not practical and routine at the present time, especially to track chemical processes with long time scales (≳1 nanosecond) and large length scales (≳10 nanometers). The repetitive and expensive DFT force computations during MD and the necessary small MD time steps (of the order of femtoseconds), lead to the primary bottlenecks of DFTbased MD simulations.…”