The Catalytic Application Testing for Accelerated Learning Chemistries via High-throughput Experimentation and Modeling Efficiently (CATALCHEM-E) program will advance the discovery and development of new heterogeneous catalysts that support the future of American fuel and chemical industries. Heterogenous catalysts are solid materials that reduce the inherent energy needed to drive industrial-scale chemical reactions responsible for making everyday products and commodities, saving manufacturers time, energy, and money. However, developing new catalysts can take decades, stifling American innovation and competitiveness.

Find the CATALCHEM-E Notice of Funding Opportunity here.

New catalysts will play a pivotal role in creating modern fuels and chemicals while mitigating supply chain risks. CATALCHEM-E focuses on using modern automation tools with artificial intelligence and machine learning (AI/ML) to expedite the discovery of these new catalyst materials.

This program will accelerate how quickly scientists and engineers can discover new, profitable catalysts for energy and chemical applications. Catalysts assist in key reactions for converting waste plastics or syngas, unlock new process technology like reactive carbon capture, and enable the production of next generation fuels, such as methanol or sustainable aviation fuel. Designing these new catalysts will strengthen U.S. manufacturing, energy independence, and national security. 

New catalyst materials typically take at least 10-15 years to develop and commercialize and rely on human-intensive lab work and expertise. Materials scientists, chemists, chemical engineers, and data scientists often work independently in academic and industry settings, hampering the necessary collaboration for innovation. CATALCHEM-E is building an entirely new approach—focused on automation, modeling, and community-building—to accelerate the timeline from 10-15 years to 12-18 months.

One promising approach for developing new catalysts is via high-throughput experimentation. This method uses robotics and other hard-tech automations to rapidly synthesize and test new catalysts in the lab. When coupled with AI algorithms, these techniques can become autonomous “self-driving laboratories,” and can accomplish in hours what would take traditional methods weeks or months.

In the first phase of the CATALCHEM-E program, multi-disciplinary project teams will train AI/ML algorithms from existing or new high-throughput experimentation data. The teams will design, create, test, analyze, and validate their models using rapid automated workflows and iterating quickly on the knowledge learned. A sub-set of teams will continue onto a second phase of the program, where they will put their new workflow to the test identifying new catalysts.  

By bolstering automation and forging new community partnerships to rapidly speed up catalyst discovery, CATALCHEM-E will: