Iowa State University is the lead institution in four research projects and university faculty are collaborators on four additional projects that are part of the U.S. Department of Energy’s (DOE) sweeping Genesis Mission. The awards were announced July 22 in Washington D.C. 

The Genesis Mission is a historic national initiative to build the world’s most powerful integrated science discovery platform by uniting government, industry, academia and philanthropy. The chosen projects will accelerate breakthroughs in energy, scientific discovery and national security through a new platform that combines artificial intelligence (AI), supercomputing, quantum systems and advanced scientific instruments.

Peter Dorhout, Iowa State’s Vice President for Research, said “As an institution, we’re honored and excited to support the Department of Energy in its historic Genesis Mission. The innovations our faculty are spearheading show how intelligent, data-rich tools can open new frontiers and accelerate discovery that delivers profound economic impact to our constituents and stakeholders throughout Iowa and around the nation.”

According to the Genesis Mission Phase I Request for Application (RFA): “The goal . . . is to identify promising pathways toward transformative scientific capabilities and establish a foundation for future investment and scale. Project teams will design and demonstrate research workflows that integrate AI with scientific investigation, while rigorously evaluating whether those approaches can accelerate discovery, improve predictive capabilities, enhance experimentation, or generate new scientific insights.”

Project title: DAISY: Decentralized Agentic Intelligence System for Scientific Inquiry 
Lead Principal Investigator (PI): Soumik Sarkar, Translational AI Center (TrAC) 
Iowa State Co-PI: Baskar Ganapathysubramanian, TrAC 
Partners: New York University and Argonne National Laboratory 

DAISY addresses a core challenge at DOE labs: today’s AI is effective for narrow tasks such as prediction, retrieval, and workflow automation, but struggles to identify knowledge gaps and propose testable ideas across distributed, multimodal scientific data (information that exists across multiple different formats or modalities simultaneously). DAISY will develop decentralized, cooperating AI agents that share compact insights to generate stronger hypotheses. A Phase I prototype will demonstrate the approach in materials research, crop breeding, and grid science, with future scale-up through Argonne’s APPFL platform.

Project title: A Spatial Generative Bayesian Computation Framework for Inferring Turbulence-Microphysics Interactions  
Lead Principal Investigator (PI): Pulong Ma, Statistics 
Iowa State Co-PI: Hongyang Gao, Computer Science  
Partner: Argonne National Laboratory

AI driven retrieval offers a path to infer cloud microphysical and dynamical states from Doppler radar data beyond the capabilities of conventional models. The project combines experts in weather science, computing, and statistics to build spatial AI tools that can estimate key cloud behaviors and measure uncertainty by comparing AI‑generated simulations with real observations. The research begins with warm clouds and later expands to mixed phase and deep convection using multimodal data across the DOE Atmospheric Radiation Measurement (ARM) network. The broader goal is to develop an easy-to-use and real-time AI retrieval framework to enable atmospheric and energy science discovery. 

Project title: AI-Enabled Discovery of Genotype-to-Phenotype Control Points for Lodging-Resistant Bioenergy Sorghum  
Lead Principal Investigator (PI): Maria Salas-Fernandez, Agronomy  
Iowa State Co-PIs: Baskar Ganapathysubramanian, TrAC; Pranav Shrotriya, Mechanical Engineering; and Anwesha Sarkar, Electrical and Computer engineering 
Partner: National Laboratory of the Rockies 

The project team will develop TITAN (Tokenized Integrated Twin for Agricultural Resilience), a scalable AI framework for predicting and designing complex biological traits by linking genetics to plant performance. Phase I will focus on resistance to lodging – a condition in which stalks bend, lean, or completely fall over, making harvest difficult – and the processing ability of sorghum for bioenergy sytems. Both are traits that arise from interactions involving genomics, cell wall chemistry, plant tissue architecture, and environmental forces. The research team’s approach will reduce data needs, improve predictive accuracy, enable inverse design, and deliver reusable Genesis‑aligned tools for engineering more resilient and efficient crops for bioenergy purposes. 

Project title: A Universal AI-Enabled Framework for the Accelerated Design, Scale-Up, and Integration of Heterogeneous Bioprocesses  
Lead Principal Investigator (PI): Iowa State University Department of Mechanical Engineering  
Iowa State Co-PI: Baskar Ganapathysubramanian, TrAC 
Partners: Lawrence Livermore National Laboratory and Quasar Energy Group (industry partner) 

The U.S. bioeconomy struggles with a “scale‑up gap,” where lab successes fail in large industrial reactors because real-world production systems behave more unpredictably. The research team will develop a Biological Digital Twin Platform that uses AI to model these complex conditions, learns from commercial bioreactor data, and optimizes biological and catalytic processes in real time. This approach replaces slow, empirical scale‑up with predictive, transferable reactor modeling and process optimization that can accelerate the production of high-value biofuels and biochemical products.

Iowa State faculty are serving as collaborators (co-PIs) on four additional Genesis Mission projects:

• Project title: From Edisonian Optimization to Predictive Molecular Design: AI-Driven Discovery and Engineering of Advanced Semiconductor Interfaces. Co-PI: Baskar Ganapathysubramanian, TrAC. Lead institution: University of Kentucky. 
• Project title: Automatic performance portability for distributed scientific workflows on the Genesis Mission Platform. Co-PI: Ali Jannesari, Computer Science. Lead institution: Battelle Memorial Institute-Pacific Northwest National Laboratory.
• Project title: Multi-Modal and Multi-Facility Application of the FM4NPP Foundation Model: Silicon Trackers and Electron Colliders. Co-PI: Marzia Rosati, Physics and Astronomy. Lead institution: Massachuetts Institute of Technology (MIT). 
• Project title: A Material-Agnostic Physics-Aware Interpretable AI Framework for Fatigue Life Prediction in TPMS Structures. Co-PI: Azadeh Sheidaei, Aerospace Engineering. Lead institution: Auburn University.