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The U.S. Department of Energy (DOE) announced on October 8, 2026 that it is awarding $159 million to 12 new Phase II projects under the Genesis Mission, its program to apply scientific AI (which DOE calls “Super Intelligence” or SI) to research problems across the national laboratories, universities, and industry. The $159 million covers only those 12 newly announced Phase II projects. It is not the total budget of the Genesis Mission.
What DOE announced
The October 8, 2026 release from the DOE Office of Science describes the following:
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- 12 new Phase II awards totaling $159 million.
- 14 Phase II projects in total once these selections are added to two projects DOE announced earlier, GridFM 2.0 and Prometheus.
- Six new Phase I awards being issued at the same time.
- 297 projects as the first-year Genesis Mission cohort, the figure given in the release subtitle.
DOE does not publish a per-project dollar amount in this announcement. Readers should therefore not divide $159 million by 12 and treat the result as the funding for any individual project; the release does not say how the money is split.
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The 12 projects and their announced aims
The table lists each newly announced Phase II project by its lead institution and the work DOE describes. These are stated aims. The release does not establish that any of them has reached its goal or produced the benefits described.
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| Field | Lead institution | Project or platform | Announced aim |
|---|---|---|---|
| Fusion energy | Commonwealth Fusion Systems | Digital twin for SPARC | An AI-enabled digital twin to simulate and optimize operations of the SPARC fusion demonstration device |
| RNA and the bioeconomy | University of California San Diego | RNA structure database expansion | Expand an RNA structure database five-fold to train SI models for resilient crops and sustainable microbes |
| Particle physics | MIT | Lattice QCD with SI | Integrate SI with supercomputing calculations in lattice quantum chromodynamics |
| Geothermal energy | University of California Irvine | MAESTRO | An SI platform to map, stimulate, and manage deep underground reservoirs |
| Critical materials | University of Illinois Urbana-Champaign | AXIS | Use SI to design electrochemical processes for extracting rare-earth elements from domestic sources |
| Enzyme design | University of Washington Seattle | Sequence-structure ensemble modeling | Predict and design functional enzymes |
| Chip design | Fermilab | AXESS | Use SI to design rugged, high-performance microchips for space, high-radiation, and ultra-cold environments |
| Particle accelerators | Lawrence Berkeley National Laboratory | MOAT-Core | A shared platform to help national laboratories run and design accelerators |
| Quantum computing | Harvard University | ASQC | Combine SI with quantum hardware to address error correction |
| Scientific software | Argonne National Laboratory | AI4HPC | A framework to modernize, optimize, and verify scientific software for supercomputers |
| Quantum materials | Oak Ridge National Laboratory | Physics-informed quantum magnet design | A physics-informed framework to design functional quantum magnets from desired properties |
| Rare isotope research | Northeastern University | Automated assistant for FRIB | Streamline operation and tuning of the Facility for Rare Isotope Beams particle separator |
Reading the headline numbers correctly
Several figures in the announcement describe different parts of the program, and they are easy to conflate.
$159 million
This is the combined amount for the 12 newly announced Phase II awards. It does not include the two earlier projects, the six Phase I awards, or the wider 297-project first-year cohort, and DOE does not state an amount for any of those groups in this release.
Rank #2
14 Phase II projects
The count is cumulative. Twelve projects are new, and two were announced before October 8, 2026. A reader comparing portfolio size should use 14 for Phase II and 297 for the first-year cohort as separate counts.
Six Phase I awards and 297 projects
DOE says it is also issuing six new Phase I awards. The release describes 297 projects as the first-year Genesis Mission cohort. The announcement does not break down how those 297 projects divide among Phase I, Phase II, or other categories, so the two figures should not be added or subtracted from each other.
What the announcement does and does not establish
The release is a primary source for four things: the award count, the total for the new Phase II awards, the lead institutions, and the stated project aims. It is also the source of the quoted statement from DOE Under Secretary for Science Dr. Darío Gil.
It does not provide grant terms, durations, individual award amounts, independent assessment, or any reported results. A project described as aiming to build a digital twin or design a new enzyme has been funded for that work; it has not, on the basis of this announcement, demonstrated that the tool works or that the expected science has been delivered.
Rank #4
Gil’s remarks in the release concern Phase II teams, scientific expertise, data, computing, and collaboration among national laboratories, universities, and industry partners. He said:
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How to compare the projects
The 12 projects span very different disciplines, so a fair comparison needs consistent criteria. Four axes work well:
- Scientific field: fusion, geothermal, critical materials, chip design, quantum computing, and so on.
- Lead institution: a private company (Commonwealth Fusion Systems), a national laboratory (Fermilab, Argonne, Lawrence Berkeley, Oak Ridge), or a university.
- Intended AI application: whether the project trains models on data, simulates a physical system, operates or tunes a facility, or designs a material, process, or device.
- Type of work: design, modeling, operations, or software.
Impact and readiness are not measurable from this source. Two projects in the same field may have very different timelines, and the announcement offers no basis for ranking them.
Why the fusion, chip design, and quantum projects stand out
The headline examples come from three areas. The Commonwealth Fusion Systems project applies AI to operating a specific device, SPARC. The Fermilab AXESS project targets chips for environments such as space and ultra-cold conditions. The Harvard ASQC project is the only one in the list that pairs AI with quantum hardware, and it targets error correction specifically. Other projects in the table cover bioengineering, geothermal energy, and rare-earth extraction, so the headline does not describe the full scope of the cohort.
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