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Accelerating Materials Innovation with DFT, CALPHAD, and AI

Yu Zhong’s MGHPCC-powered simulations, thermodynamic modeling, and AI are accelerating the design of energy and manufacturing materials.

Designing new materials has traditionally required years of experimental trial and error. Researchers increasingly use high-performance computing to shorten this process, predicting how materials will behave before they are synthesized in the laboratory.

At Worcester Polytechnic Institute, Yu Zhong and his team are developing an integrated materials-design platform that combines first-principles quantum mechanical simulations, CALPHAD thermodynamic modeling, artificial intelligence, and experimental validation. By bringing these approaches together, the group is creating a more efficient pathway for discovering and optimizing advanced materials for energy technologies, manufacturing, and extreme environments.

A central component of the platform is large-scale density functional theory (DFT) simulation, performed using high-performance computing resources. These calculations provide detailed insights into material stability, phase transformations, atomic diffusion, defects, interfaces, and functional properties. The resulting data are combined with thermodynamic models and machine learning tools to enable faster exploration of complex materials systems and guide experimental investigations.

The platform is being applied to a diverse set of challenges. Recent work has improved computational descriptions of chromium and chromium-rich alloys that are important to many structural materials. Other projects have developed AI-assisted tools that simplify and automate complex materials simulations, explored new approaches to designing high-entropy alloys, and investigated materials for solid oxide fuel cells, electrolysis systems, and hydrogen-energy technologies. Researchers have also created computational frameworks for engineering material interfaces, a key challenge in next-generation energy devices.

Together, these efforts demonstrate how advanced computing is transforming materials science. By integrating physics-based simulation, thermodynamic modeling, and artificial intelligence into a unified workflow, the team is helping accelerate the discovery of materials needed for cleaner energy, advanced manufacturing, and future technologies.

Yu Zhong
Yu Zhong is an Associate Professor of Mechanical and Materials Engineering at Worcester Polytechnic Institute whose research integrates computational thermodynamics, first-principles modeling, machine learning, and experiments to accelerate materials discovery.

Research projects

Computation + Machine Intelligence | Wu Tsai Institute
AI Pareidolia
Monte Carlo eXtreme (MCX) - a Physically-Accurate Photon Simulator
Supporting Data-intensive Social Science
Discovering Evolution’s Master Switches
Electron Heating in Kinetic-Alfvén-Wave Turbulence
Dexterous Robotic Hands
The Kempner Institute - Unlocking Intelligence
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Collaborative projects

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OUTREACH & EDUCATION PROJECTS

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