Texas A&M Joins Genesis Mission to Use Artificial Intelligence for Science

Texas A&M University has joined the U.S. Department of Energy’s (DOE) Genesis Mission, a national initiative designed to create a unified, AI-enabled platform for science discovery at unprecedented scale. The goal is to accelerate breakthroughs that span energy, foundational research, and national security, by connecting vast DOE data resources, advanced instruments, and high-performance computing with state-of-the-art artificial intelligence.

Genesis Mission is framed as an integrated discovery engine: government, industry, academia, and philanthropy collaborate to tackle 26 technical science and technology challenges. The DOE selected 278 projects at the program’s outset, emphasizing not just individual excellence, but the ability to coordinate expertise across disciplines.

Rather than prioritizing a single field, the mission is organized into three pillars: energy dominance, discovery science, and national security. Challenge areas include modernizing and scaling the electric grid, securing critical mineral supply chains, advancing nuclear fission and fusion technologies, and improving quantum computing and advanced manufacturing.

A central technical premise underpins all pillars—linking scientific data streams with compute and AI systems into a “discovery platform” that can shorten the time from hypothesis to validated insight. This requires treating experiments and datasets as part of an interoperable pipeline: data ingestion, model training, simulation, and decision support all become connected workflows.

Texas A&M’s contribution reflects that systems approach. Four Texas A&M projects were selected, each aiming to turn high-dimensional scientific information into actionable guidance—using AI for mineral recovery, for precision experimental design, and for safer, faster nuclear licensing workflows.

One project targets critical minerals using AI-guided biomining. By integrating microbial activity, hydrological behavior, and geochemical signatures, researchers aim to pinpoint where microorganisms and water movement can accelerate the extraction process. The approach is designed to improve both efficiency and sustainability in resource development.

Another project applies scalable agentic “digital twins” to autonomous precision facilities for nuclear physics experiments. Because these experiments are data-scarce—often running only limited campaigns—traditional data-hungry AI is inadequate; instead, the work focuses on simulation-linked intelligence that can make scarce beam time more productive.

In the nuclear domain, a team is building SHIELD, a human-in-the-loop AI system that automates engineering analysis and licensing documentation for advanced reactor technologies. By generating model outputs and drafting documentation while maintaining expert oversight and verification, the project seeks to reduce years of repetitive analysis without compromising safety.

Finally, a geology-focused effort uses multimodal AI to search for rare earth element deposits across Texas, the Colorado Mineral Belt, and the U.S. Southwest. By combining geological records, satellite observations, geochemical and geophysical data, the team looks for patterns that indicate where similar conditions could exist.