Materials for Energy

Investigating new ionic, polymeric, and Lewis acidic dopants that increase conductive polymer conductivity and stability while retaining ease of processing.


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Hydrogen will play a critical role in the transition away from fossil fuels. There are several challenges that need to be addressed before it can be used at scale, including its cost-effective production. The Entropy for Energy Laboratory employs state-of-the-art data-driven methods to design promising new hydrogen-generating materials.


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Investigating exothermic reactions in a variety of layered, material systems.


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The energy powering industrial processes is largely dissipated as waste heat. The Entropy for Energy Laboratory employs state-of-the-art data-driven methods to design materials that can harness and reutilize that energy and improve these systems’ overall efficiencies.


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Exploring new and complicated nanoparticulate structures displaying photochromic behavior in fluorocarbon-based polymers for energy-saving applications.


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Tuning the energy levels and charge densities in polymer blends to address worldwide energy challenges.


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Continued miniaturization of electronics is pushing the boundaries of energy storage devices. The next generation of devices depends on enhanced capacity, lifespan, weight, and safety. The Entropy for Energy Laboratory employs state-of-the-art data-driven methods to explore new chemistries addressing these challenges.


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Molecular design and nanostructure approaches to maximizing polymer capacitor energy density by optimizing relative permittivity and dielectric breakdown strength.


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The Entropy for Energy Laboratory employs computational modeling, generative modeling, and data-driven discovery to design and predict new materials capable of securely immobilizing nuclear waste over long timescales. Emphasis is placed on compounds stabilized by atomic-scale disorder to support safe, sustainable nuclear technology.


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Next-generation fusion reactors demand materials that maintain exceptional stability under extreme temperatures and radiation. The Entropy for Energy Laboratory integrates machine learning, generative modeling, and high-throughput simulation to accelerate the identification and optimization of advanced materials for fusion energy systems.


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Advanced capacitive energy storage is a key enabler for modern electronics and power systems. The Entropy for Energy Laboratory utilizes computational techniques and generative modeling to discover and design novel dielectric materials with improved permittivity, breakdown strength, and reliability for high-performance, safe, and efficient energy storage technologies.


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By harnessing artificial intelligence, generative modeling, and automation, the Entropy for Energy Laboratory explores vast chemical spaces and rapidly predicts, generates, and optimizes material properties. This approach expedites the discovery and deployment of advanced materials for diverse energy applications.


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