Ai-assisted discovery of high-temperature dielectrics for energy storage

Rational Co‐Design of Polymer Dielectrics for Energy Storage

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. Although traditional materials discovery has historically benefited from

Anisotropic Semicrystalline Homopolymer Dielectrics for High

High-temperature dielectric polymers are in high demand for powering applications in extreme environments. Here, we have developed high-temperature homopolymer dielectrics with anisotropy by leveraging the hierarchical structure in semicrystalline polymers. The lamellae have been aligned parallel to the surface in the dielectric films.

佐治亚理工、清华团队用AI辅助发现储能新材料,登Nature子刊

该研究以「AI-assisted discovery of high-temperature dielectrics for energy storage」为题,于 2024 年 7 月 19 日发布在《Nature Communications》。 静电电容器需要新材料. 静电电容器作为现代电气系统中的储能设备发挥着至关重要的作用。

AI-assisted discovery of high-temperature dielectrics for energy

Dielectrics are essential for modern energy storage, but currently have limitations in energy density and thermal stability. Here, the authors discover dielectrics with 11 times the energy...

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

Rishi Gurnani & Stuti Shukla & Deepak Kamal & Chao Wu & Jing Hao & Christopher Kuenneth & Pritish Aklujkar & Ashish Khomane & Robert Daniels & Ajinkya A. Deshmukh & Yang Cao & Gregory Sotzing & Rampi, 2024. "AI-assisted discovery of high-temperature dielectrics for energy storage," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

Superior dielectric energy storage performance for high-temperature

Electrostatic capacitors are critical components in a broad range of applications, including energy storage and conversion, signal filtering, and power electronics [1], [2], [3], [4].Polymer-based materials are widely used as dielectrics in electrostatic capacitors due to their high voltage resistance, flexibility and cost-effectiveness [5], [6], [7].

Phase-field modeling and machine learning of electric-thermal

Polymer dielectrics are promising for high-density energy storage but dielectric breakdown is poorly understood. AI-assisted discovery of high-temperature dielectrics for energy storage

Design of polymers for energy storage capacitors using machine learning

AI-assisted discovery of high-temperature dielectrics for energy storage polymer dielectrics for high-temperature capacitors need to meet multiple property criteria, including a high energy density to reduce the size of capacitors, high thermal stability to survive high operating temperatures, and high breakdown field strength to withstand

Publications | Electrical Insulation Research Center

[3] Yifei Wang, Zongze Li, Chao Wu, Peinan Zhou, Jierui Zhou, Jindong Huo, Kerry Davis, Antigoni Konstantinou, Hiep Nguyen, and Yang Cao, "High-performance polymer dielectric with montmorillonite nanosheets coating for high-temperature energy storage ", Chemical Engineering Journal, Vol. 437, 135430, 2022.

AI-assisted discovery of high-temperature dielectrics for

Many of the discovered dielectrics exhibit high ther-mal stability and high energy density over a broad temperature range. One such dielectric displays an energy density of 8.3 J cc−1 at...

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

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.

AI-assisted discovery of high-temperature dielectrics for energy

This work was financially supported by the Office of Naval Research through a Multi-University Research Initiative (MURI) grant (N00014-17-1-2656), the Center for Understanding and

AI accelerates discovery of next-gen polymers

"The new class of polymers with high energy density and high thermal The potential for real-world translation of AI-assisted materials AI-assisted discovery of high-temperature dielectrics

Frequency-dependent dielectric constant prediction of polymers

AI-assisted discovery of high-temperature dielectrics for energy storage Article Open access 19 July 2024 Designing polymer nanocomposites with high energy density using machine learning

AI for dielectric capacitors

Here, P max and P r represent the maximum polarization and remanent polarization, and η denotes the energy efficiency. These equations demonstrate that high P max, low P r and high dielectric breakdown field E b are conducive to achieving higher energy density and energy efficiency in dielectric materials. Owing to the rich characteristics of multiscale

Enhanced high-temperature energy storage performances in

Polymer dielectrics are considered promising candidate as energy storage media in electrostatic capacitors, which play critical roles in power electrical systems involving elevated temperatures

性能强11倍,佐治亚理工、清华团队用AI辅助发现储能新材料,登

该研究以「AI-assisted discovery of high-temperature dielectrics for energy storage」为题,于 2024 年 7 月 19 日发布在《Nature Communications》。 静电电容器需要新材料. 静电电容器作为现代电气系统中的储能设备发挥着至关重要的作用。

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

AI-assisted discovery of high-temperature dielectrics for energy storage. Overview of attention for article published in Nature Communications, July 2024. AI-assisted discovery of high-temperature dielectrics for energy storage Published in: Nature Communications, July 2024 DOI: 10.1038/s41467-024-50413-x:

Rishi Gurnani on LinkedIn: AI-assisted discovery of high-temperature

AI-assisted discovery of high-temperature dielectrics for energy storage - Nature Communications This multi-disciplinary AI-assisted design and validation journey has led to a remarkable class

High-Energy-Density and High Efficiency Polymer Dielectrics for High

Polymer dielectrics are key components for electrostatic capacitors in energy, transportation, military, and aerospace fields, where their operation temperature can be boosted beyond 125 °C. While most polymers bear poor thermal stability and severe dielectric loss at elevated temperatures, numerous linear polymers with linear D-E loops and low dielectric

Prediction of high-temperature polymer dielectrics using a

Machine learning has shown its great potential in the accelerated discovery of advanced materials in the field of computational molecular design. High-temperature polymer dielectrics are urgently required with the emerging applications of energy-storage dielectric film capacitors under high-temperature conditions. Here, we demonstrate the successful prediction

Ai-assisted discovery of high-temperature dielectrics for energy storage

6 FAQs about [Ai-assisted discovery of high-temperature dielectrics for energy storage]

Are dielectrics a viable alternative to commercial energy storage?

Dielectrics are essential for modern energy storage, but currently have limitations in energy density and thermal stability. Here, the authors discover dielectrics with 11 times the energy density of commercial alternatives at elevated temperatures.

Why is a high thermal stability dielectric important?

For example, the high thermal stability of each dielectric in Fig. 3 b eliminates the need for capacitor cooling systems. Among these dielectrics, those with higher Ue are preferred, as this attribute reduces the amount of capacitor material required to store a fixed amount of energy.

Can artificial intelligence help converge on high-performance dielectrics?

Within the vast expanse of chemical possibilities for all polymers, it is likely that a wide variety of high-performance dielectrics await discovery. Well-trained and calibrated artificial intelligence (AI), capable of handling large numbers that challenge human imagination, can help converge on extraordinary materials rapidly.

How can a new generation of AI improve materials discovery?

Any of these approaches to materials discovery would benefit from increased accuracy of the polyGNN models—perhaps using strategies like pretraining 51, 52 —to reduce the amount of time spent on bad leads. Moreover, significant resources should be dedicated to a new generation of AI characterized by human interpretability.

Can machine learning be used to design polymers for energy storage capacitors?

Kern, J., Chen, L., Kim, C. & Ramprasad, R. Design of polymers for energy storage capacitors using machine learning and evolutionary algorithms. J. Mater. Sci. 56, 19623–19635 (2021). Gurnani, R. et al. polyG2G: a novel machine learning algorithm applied to the generative design of polymer dielectrics.

Does polyverse work in high-temperature dielectric search?

This study introduces the polyVERSE ("polymers designed by Virtually-Executed Rule-Based Synthesis Experiments”) paradigm (Fig. 1 a), showcasing its success in achieving these four attributes in the context of high-temperature dielectric search.

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