Computational Materials Science

We are developing techniques that aim to extend the time scales accessible with simulation.


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Developing model system for studying the kinetics of exothermic phase transitions under conditions of rapid thermal and mechanical loading using metallic multilayer foils.


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Providing students with experience with simulation and modeling of materials and evaluating the extent to which this revision improves the assimilation of core MSE concepts and the students’ lifelong learning goals.

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Investigating the influence of block architectures on mechanical properties and molecular chain movement using molecular dynamics simulations.


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Developing computer programs called "meta-codes" for predicting the way small and nanometer-scale features of materials evolve over time.


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Hydrogen will play a critical role in the transition away from fossil fuels. There are several callenges 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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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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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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