Inorganometallic Catalyst Design Center
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Inorganometallic Catalyst Design Center (ICDC) is an Energy Frontier Research Center funded by the U.S. Department of Energy, hosted by the University of Minnesota.
ICDC focuses on the discovery of novel catalytic materials through a synergy of computational science, artificial intelligence, and experimental validation, targeting the conversion of natural gas to higher-value chemicals and fuels.
Mission
The mission of the ICDC is to accelerate the discovery of catalytic materials that can efficiently convert natural gas (primarily methane) into more valuable chemicals like alcohols. By employing advanced computational techniques, including machine learning and density functional theory, ICDC seeks to understand the intricate relationship between catalyst structure and its function, thereby paving the way for new catalyst designs with enhanced performance and sustainability"About ICDC". Inorganometallic Catalyst Design Center. https://icdc.umn.edu/about..
Parent organization
ICDC is led by the University of Minnesota and is part of the U.S. Department of Energy's Office of Science initiative, specifically under the Energy Frontier Research Centers program. This program aims to tackle major scientific challenges in energy.
Legislation
ICDC was established through funding from the U.S. Department of Energy's Office of Science, without specific legislative action but as part of broader efforts to advance energy research.
Partners
- Argonne National Laboratory
- Northwestern University
- University of California, Davis
- Massachusetts Institute of Technology
- Other academic and research institutions for collaborative projects
Number of employees
Exact numbers of employees aren't specified, but ICDC involves a collaborative team of researchers from various institutions.
Organization structure
ICDC's structure revolves around:
- Computational and Theoretical Research: Using AI and modeling to predict catalyst behavior.
- Experimental Validation: Synthesizing and testing new catalysts based on computational predictions.
- Cross-disciplinary Teams: Combining expertise from chemistry, materials science, and engineering.
Leader
ICDC is headed by a [Director].
Divisions
The center does not have traditional divisions but operates through:
- Computational Modeling: For catalyst design and prediction.
- Experimental Synthesis and Testing: To validate computational findings.
- Data Science and Machine Learning: To accelerate discovery processes"Research". Inorganometallic Catalyst Design Center. https://icdc.umn.edu/research..
List of programs
- Catalyst discovery from natural gas conversion
- Machine learning for catalysis research
- Collaborative research projects with partner institutions
Last total enacted budget
ICDC received a $12 million grant over four years from the U.S. Department of Energy to continue its research"ICDC receives $12M renewal grant from U.S. Department of Energy". University of Minnesota College of Science and Engineering. https://cse.umn.edu/college/news/icdc-renewal..
Staff
ICDC operates with a team of researchers including faculty, postdocs, and students from the University of Minnesota and its partner institutions.
Funding
The primary funding for ICDC comes from the U.S. Department of Energy, supporting its research into new catalytic materials and processes.
Services provided
ICDC provides services by developing new catalysts and methodologies for natural gas conversion, leveraging computational tools to design materials with specific properties for energy applications, and sharing knowledge through publications and collaborations.
Regulations overseen
ICDC does not oversee regulations but works within the scientific research frameworks established by the DOE.
Headquarters address
207 Pleasant St SE, Minneapolis, MN 55455
History
ICDC was established in 2014 as part of the DOE's Energy Frontier Research Centers, focusing on a computationally-guided approach to catalyst discovery, which has since expanded to include artificial intelligence and machine learning techniques for enhanced material prediction and design.