Welcome to the MGHPCC Virtual Booth

Worcester Polytechnic Institute

Developing Advanced Materials for a Sustainable Energy Future

Fanglin Che and her group use computers at the MGHPCC in their work centered around AI-guided multi-scale and multi-physics simulations of catalysis and material science.

The usability and costly storage issues of renewable electricity from solar or wind energy become major challenges on a global scale due to the daily and seasonal variability of sunlight or wind and the geographic inequality of energy needs. A promising solution to address the above challenges lies in electrified modular chemical processes, which provide a sustainable approach to store intermittent energy chemically.

Theoretically determining and quantifying the roles of electrified interfacial structure and field-dipole interactions on controlling the activity and selectivity of chemical processes and then integrating these roles to establish deep collaborations between physics-informed, interpretable machine learning and electrified interfacial chemical processes is crucial for rationally designing catalysts for these electrified modular systems for energy storage and sustainable chemical production.

Research in the Che Group has focused on several systems that take advantage of process intensification by converting abundant resources, e.g., carbon dioxide, natural gas, and nitrogen, into valuable chemicals. The systems include: (1) organic-inorganic interface and its impact on carbon reactive capture and selective conversion; (2) field-dipole interaction effects on ammonia decomposition and synthesis for hydrogen utilization, storage, and production.

Copper makes a great catalyst for turning carbon dioxide into useful chemicals, but it has some limitations. To improve its catalyzing qualities, researchers have added tiny amounts of platinum or similar metals to copper. This combination helps the copper convert CO2 into chemicals instead of making hydrogen, which is an unwanted side reaction. The researchers found that introducing small clusters of platinum or palladium onto copper surfaces can effectively produce chemicals like methane and ethylene. This new approach gives more options for using copper in CO2 reduction.

Fanglin Che
Associate Professor, Worcester Polytechnic Institute, Fanglin Che is a chemical engineer advancing AI-guided catalysis through physics-informed machine learning, computational chemistry, and multiscale simulation to accelerate clean energy, electrified chemical processes, and sustainable materials discovery.

Principal Members

Yale

Featured Projects

A Cooler Quantum Future
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A Safer Way to See Inside Cells
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Adaptive Deep Learning Systems Towards Edge Intelligence
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AI for Cancer Diagnosis
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AI Pareidolia
AI-powered Performance Analytics for HPC Systems
Asking Planetary Whatifs
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Better Pathogen Targeting
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Building Bicontinuous Materials
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Building for Floods
Computation + Machine Intelligence | Wu Tsai Institute
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Computing Hidden Health Threats from Heat
Computing Ocean Carbon Removal
Computing the Geometry of Frustration
Dartmouth Chat
Developing Advanced Materials for a Sustainable Energy Future
From Murmurations to Music: Turning bird flight into climate stories
Generations of Discovery
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Harvard FASRC
IceCube: Hunting Neutrinos
Imaging at Light Speed
Impact of Marine Heatwaves on Coral Diversity
Inside the Ocean's Response to Hurricanes
Learning Beyond Attention
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Lincoln Laboratory Supercomputing Center (LLSC)
Mapping the Universe with AI and Supercomputing
Materials Remember
Materials That Give
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MGHPCC AI Computing Resource (AICR)
MGHPCC and Internet2
MGHPCC and Northern Crossroads
MGHPCC and the Campus Research Computing Consortium
MGHPCC and the IEEE High Performance Computing Conference
MGHPCC and the Massachusetts AI Hub
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MGHPCC and the National Data Platform
MGHPCC and the Northeast Research and Education Network
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MGHPCC and the Open Storage Network
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MGHPCC and the Prototype National Research Platform (PNRP)
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Microplastic-Free by Design
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Multifunctional 3D-Printed Materials
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Naval and Ocean Renewable Energy Hydrodynamics
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NEU Research Computing
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New Insights on Binary Black Holes
Northeast Storage Exchange
Precision Molecular Editing
Reading Night Light
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ReMIND
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Rhode Island INBRE Molecular Informatics Core (MIC)
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Sailing the Symbiosis Seascape
Seeing Ocean Currents with AI
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Shining a Light on Dark Matter
Simulating Large Biomolecular Assemblies
Smarter Protein Engineering
Software for Unreliable Quantum Computers
Solving a Physics Mystery with Supercomputing
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Staving off the Banana Apocalypse
Supercomputing the Arctic
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Supporting Data-intensive Social Science
The Kempner Institute - Unlocking Intelligence
The US ATLAS Northeast Tier 2 Center
Towards a Self-driving Lab?
Tracking Earth's Atmospheric Highways
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UMass - URI Unity Cluster
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UMass Amherst Research Computing and Data
Unlocking Non-coding DNA
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URI Institute for AI & Computational Research
Virtual Materials Discovery at Scale
Watering MIT's AI Garden
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Wrangle Range Modeling
Yale Budget Lab
Yale Center for Genome Analysis (YCGA)
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Yale Center for Research Computing
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