AI-assisted discovery of high-temperature dielectrics for energy storage.

Electrostatic capacitors play a crucial role as energy storage devices in modern electrical systems. Energy density, the figure of merit for electrostatic capacitors, is primarily determined by the choice of dielectric material. Most industry-grade polymer dielectrics are flexible polyolefins or rigid aromatics, possessing high energy density or high thermal stability, but not both. Here, we employ artificial intelligence (AI), established polymer chemistry, and molecular engineering to discover a suite of dielectrics in the polynorbornene and polyimide families. Many of the discovered dielectrics exhibit high thermal stability and high energy density over a broad temperature range. One such dielectric displays an energy density of 8.3 J cc-1 at 200 °C, a value 11 × that of any commercially available polymer dielectric at this temperature. We also evaluate pathways to further enhance the polynorbornene and polyimide families, enabling these capacitors to perform well in demanding applications (e.g., aerospace) while being environmentally sustainable. These findings expand the potential applications of electrostatic capacitors within the 85-200 °C temperature range, at which there is presently no good commercial solution. More broadly, this research demonstrates the impact of AI on chemical structure generation and property prediction, highlighting the potential for materials design advancement beyond electrostatic capacitors.

High dielectric constant and high breakdown strength polyimide via tin complexation of the polyamide acid precursor.

Polymer dielectrics with ultra-high charge-discharge rates are significant for advanced electrical and electronic systems. Despite the fact that polymers possess high breakdown strength, the low dielectric constant (k) of polymers gives rise to low energy densities. Incorporating metal into polyimides (PI) at the polyamic acid (PAA) precursor stage of the synthetic process is a cheap and versatile way to improve the dielectric constant of the hybrid system while maintaining a high breakdown strength. Here, we explore inclusion of different percentages of Sn as a coordinated complex in a polyimide matrix to achieve metal homogeneity within the dielectric film to boost dielectric constant. Sn-O bonds with high atomic polarizability are intended to enhance the ionic polarization without sacrificing bandgap, a measurable property of the material to assess intrinsic breakdown strength. Enhancements of k from ca. 3.7 to 5.7 were achieved in going from the pure PI film to films containing 10 mol% tin.

Deep Well Trapping of Hot Carriers in a Hexagonal Boron Nitride Coating of Polymer Dielectrics.

Polymer dielectrics can be cost-effective alternatives to conventional inorganic dielectric materials, but their practical application is critically hindered by their breakdown under high electric fields driven by excited hot charge carriers. Using a joint experiment-simulation approach, we show that a 2D nanocoating of hexagonal boron nitride (hBN) mitigates the damage done by hot carriers, thereby increasing the breakdown strength. Surface potential decay and dielectric breakdown measurements of hBN-coated Kapton show the carrier-trapping effect in the hBN nanocoating, which leads to an increased breakdown strength. Nonadiabatic quantum molecular dynamics simulations demonstrate that hBN layers at the polymer-electrode interfaces can trap hot carriers, elucidating the observed increase in the breakdown field. The trapping of hot carriers is due to a deep potential well formed in the hBN layers at the polymer-electrode interface. Searching for materials with similar deep well potential profiles could lead to a computationally efficient way to design good polymer coatings that can mitigate breakdown.

Tuning Surface States of Metal/Polymer Contacts Toward Highly Insulating Polymer-Based Dielectrics.

Metal-polymer interface plays a crucial role in controlling the dielectric performance in all flexible electronics. Ideally, the formation of the Schottky barrier due to the large band offset of the electron affinity of the polymer over the work function of the electrode should sufficiently impede the charge injection. Arguably, however, such an injection barrier has hardly been indisputably verified in polymer-metal junctions due to the ever-existing surface states, which dramatically compromise the barrier thus leading to undesired high electrical conduction. Here, we demonstrate experimentally a clear negative correlation between the breakdown strength and the density of surface states in polymer dielectrics. The existence of surface states reduces the effective barrier height for charge injection, as further revealed by density functional theory calculations and photoinjection current measurements. Based on these findings, we present a surface engineering method to enhance the breakdown strength with the application of nanocoatings on polymer films to eliminate surface states. The density of surface states is reduced by 2 orders of magnitude when the polymer is coated with a layer of two-dimensional hexagonal boron nitride nanosheets, leading to about 100% enhancement of breakdown strength. This work reveals the critical role played by surface states on electrical breakdown and provides a versatile surface engineering strategy to curtail surface states, broadly applicable for all polymer dielectrics.

Dielectric Polymers Tolerant to Electric Field and Temperature Extremes: Integration of Phenomenology, Informatics, and Experimental Validation.

Flexible polymer dielectrics tolerant to electric field and temperature extremes are urgently needed for a spectrum of electrical and electronic applications. Given the complexity of the dielectric breakdown mechanism and the vast chemical space of polymers, the discovery of suitable candidates is nontrivial. We have laid the foundation for a systematic search of the polymer chemical space, which starts with "gold-standard" experimental measurements and data on the temperature-dependent breakdown strength (Ebd) for a benchmark set of commercial dielectric polymer films. Phenomenological guidelines are derived from this data set on easily accessible properties (or "proxies") that are correlated with Ebd. Screening criteria based on these proxy properties (e.g., band gap, charge injection barrier, and cohesive energy density) and other necessary characteristics (e.g., a high glass transition temperature to maintain the thermal stability and a high dielectric constant for high energy density) were then setup. These criteria, along with machine learning models of these properties, were used to screen polymers candidates from a candidate list of more than 13 000 previously synthesized polymers, followed by experimental validation of some of the screened candidates. These efforts have led to the creation of a consistent and high-quality data set of temperature-dependent Ebd, and the identification of screening criteria, chemical design rules, and a list of optimal polymer candidates for high-temperature and high-energy-density capacitor applications, thus demonstrating the power of an integrated and informatics-based philosophy for rational materials design.

Novel high voltage polymer insulators using computational and data-driven techniques.

One of the key bottlenecks in the development of high voltage electrical systems is the identification of suitable insulating materials capable of supporting high voltages. Under high voltage scenarios, conventional polymer based insulators, which are one of the popular choices of insulators, suffer from the drawback of space charge accumulation, which leads to degradation in desirable electronic properties and facilitates dielectric breakdown. In this work, we aid the development of novel polymers for high voltage insulation applications by enabling the rapid prediction of properties that are correlated with dielectric breakdown, i.e.,the bandgap (Egap) of the polymer and electron injection barrier (Φe) at the electrode-insulator interface. To accomplish this, density functional theory based methods are used to develop large, chemically diverse datasets of Φe and Egap. The deviation of the computed properties from experimental observations is addressed using a statistical technique called Bayesian calibration. Furthermore, to enable rapid estimation of these properties for a large set of polymers, machine learning models are developed using the created dataset. These models are further used to predict Egap and Φe for a set of 13k previously known polymers. Polymers with high values of these properties are selected as potential high voltage insulators and are recommended for synthesis. Finally, the models developed here are deployed at www.polymergenome.org to enable the community use.

Computable Bulk and Interfacial Electronic Structure Features as Proxies for Dielectric Breakdown of Polymers.

Breakdown strength, the maximum electric field that can be applied on a dielectric polymer without destroying its insulating characteristics, sets an upper limit on the maximum energy that can be stored in this material. Despite its significance, the breakdown strength remains poorly understood and impractical to compute. This is a major challenge in the development of high-energy dielectric polymers for which a large number of candidates must be screened for identifying those with high breakdown strength. In this work, we develop a multistep strategy for accessing the breakdown strength through two proxies that can be computationally estimated in a high-throughput manner, i.e., the polymer band gap and electron injection barrier at electrode-polymer interfaces. First, these properties are experimentally proven (established) to be correlated strongly with the breakdown strength of a number of benchmark polymers. Then, we develop a simple model, which relies on the chain structure of polymers, to estimate their band gap and electron injection barrier at the level of density functional theory. After validation, this model was finally used for 990 polymers, identifying 53 candidates that have preferable proxies, and thus, potentially having high breakdown strength. Because of the past synthesizability evidence of these polymers, we hope that they may be considered to be synthesized and tested in the near future. Moreover, some empirical rules that were extracted from our computed data could be useful for polymer selection and design in general. We note that the strategy used here is generic and can be used to design materials with other attractive, but complex, properties as well.