MIT researchers are set to contribute to the U.S. Department of Energy’s (DOE) Genesis Mission, with 15 collaborative projects among those selected for funding under Genesis Phase I, DOE announced Wednesday.
The Genesis Mission, a national initiative, intends to build “the world’s most powerful integrated science discovery platform” by incentivizing cross-sector collaborations that leverage AI, supercomputing, quantum systems, and advanced scientific instruments to accelerate breakthroughs in energy, scientific discovery, and national security.
“MIT researchers are proud to be leading and contributing to projects under the Genesis Mission, in vital areas of research that support national priorities,” says Ian A. Waitz, MIT’s vice president for research. “The Genesis Mission represents a fantastic opportunity to catalyze the power of universities, industry, and the U.S. national laboratories to advance science, technology, and innovation for the benefit of the nation and the world.”
The DOE announced the initial projects during its Genesis Summit in Washington on Wednesday. The research funding to MIT is pending completion of negotiations toward an award agreement for each project. In phase I, funded project teams will work to demonstrate research workflows that integrate AI with scientific investigation, and to rigorously evaluate the scientific merit of their approach.
Projects under the Genesis Mission are collaborative by design; teams must draw on the expertise of researchers from academia, industry, and/or the national laboratories. Among the selected phase I projects with MIT involvement are those that aim to develop powerful quantum sensors to help explain fundamental questions about the universe; advance knowledge of chemical-free methods to extract rare earth elements; model the behavior of plasma in fusion tokamaks and future fusion reactors; develop digital twins for fusion magnet systems; exploit the self-assembly of biomolecules to design materials with targeted properties; generatively design rotating blades for machinery systems; and more. Phase I projects that identify promising pathways toward transformative capabilities at scale may be considered by DOE for further Genesis Mission funding.
Six of the selected projects are to be led by MIT principal investigators (PIs):
- AI-Driven Discovery of Electrochemical Separation Methods for Rare Earth Elements
MIT lead: Martin Bazant (Department of Chemical Engineering, ChemE), Chevron Professor in Chemical Engineering and professor of mathematics
- AI for Learning Missing Constitutive Structure in Fracture Models
MIT lead: Laurent Demanet (Department of Earth, Atmospheric and Planetary Sciences), professor of applied mathematics in the Department of Mathematics and co-director of the MIT Center for Computational Science and Engineering
- AI-Driven Quantum Sensing for Precision Tests of Fundamental Physics
MIT lead: Ronald Garcia Ruiz (Laboratory for Nuclear Science, LNS), associate professor of physics and Thomas A. Frank (1977) Career Development Professor
- Multi-Agent Inverse Design of Block Polypeptoids Into Hierarchical Nanostructures
MIT lead: Bradley Olsen (ChemE), Alexander and I. Michael Kasser (1960) Professor
- CATALYST: Core Accelerated Trajectories with Augmented Learning bY Sim-to-experiment Transfer
MIT lead: Cristina Rea (Plasma Science and Fusion Center), principal research scientist and division head for data science
- Multi-Modal and Multi-Facility Application of the FM4NPP Foundation Model: Silicon Trackers and Electron Colliders
MIT lead: Gunther Roland (LNS), professor of physics and division head for experimental nuclear and particle physics
MIT researchers are expected to participate in another nine selected projects led by other institutions, companies, and labs:
- Framework for Optimized Rotating Blade Design Using Generative Engineering (FORGE)
Project lead: GE Vernova Advanced Research Center
MIT lead: Faez Ahmed (Department of Mechanical Engineering), associate professor of mechanical engineering and the Esther and Harold E. Edgerton Career Development Professor
- Superconducting Polychronous Computation Near Criticality
Project lead: Argonne National Laboratory
MIT lead: Karl Berggren (Research Laboratory of Electronics), the Julius A. Stratton Professor in Electrical Engineering and Physics
- Scalable Agentic Digital Twins for Autonomous Precision Facilities
Project lead: Texas A&M University
MIT lead: Ronald Garcia Ruiz (LNS)
- Agentic AI for Real-Time Expedited Discovery from High-Complexity EIC Data Streams
Project lead: Purdue University
MIT lead: Philip Harris (LNS), associate professor of physics
- Self-Driving Discovery and Co-Design of MXene Memristors for 3D Compute-in-Memory Systems
Project lead: Northeastern University
MIT lead: Ju Li (Department of Nuclear Science and Engineering, NSE), the Carl Richard Soderberg Professor in Power Engineering and professor of materials science and engineering
- A-WILD: AI-driven Workflows for Intelligent Lab Discovery
Project lead: Lawrence Berkeley National Laboratory (LBNL)
MIT lead: Ju Li (NSE)
- A Foundational Generative AI Framework to Advance Water-Energy Security
Project lead: LBNL
MIT lead: Haruko Wainwright (NSE), Atlantic Richfield Career Development Professor in Energy Studies, assistant professor of nuclear science and engineering, and assistant professor of civil and environmental engineering
- An AI-Driven Platform for HLW Repository Design and Analysis with Digital Twins, GIS Data Integration, and Surrogate Models
Project lead: LBNL
MIT lead: Haruko Wainwright (NSE)
- Toward Physics-Informed Digital Twins for Fusion Magnet Systems
Project lead: LBNL
MIT lead: Holger Witte (LNS), associate director of MIT’s Bates Research and Engineering Center.
“The extraordinary response to this Genesis Mission application process demonstrates that America’s scientific community is ready to reimagine how discovery happens,” said DOE Under Secretary Darío Gil SM ’00 PhD ’03, in the DOE’s announcement. “Through the Genesis Mission, we are bringing together the nation’s leading researchers, institutions, and technology partners to build the next generation of scientific capability. We look forward to seeing these teams demonstrate new research workflows that accelerate discovery and reveal what is possible when AI and science advance together.”
A complete list of the first Genesis Mission projects selected for award negotiations is available from the U.S. Department of Energy.
Facts Only
* MIT researchers are set to contribute to the DOE’s Genesis Mission via 15 collaborative projects in Phase I.
* The Genesis Mission intends to build an integrated science discovery platform using AI, supercomputing, quantum systems, and advanced instruments.
* Phase I funding requires teams to demonstrate research workflows integrating AI with scientific investigation and evaluate scientific merit.
* Projects are collaborative, drawing expertise from academia, industry, and/or national laboratories.
* Selected projects include developing quantum sensors, chemical-free extraction methods for rare earth elements, plasma modeling for fusion reactors, digital twins for fusion magnets, and biomolecule self-assembly for material design.
* Six Phase I projects are led by MIT principal investigators, including leads on AI discovery of separation methods, AI for fracture models, quantum sensing, inverse design, and various agentic AI workflows.
* Other selected projects involve collaborations with entities like GE Vernova, Argonne National Laboratory, Texas A&M University, Purdue University, Northeastern University, and LBNL.
* Specific MIT leads include Martin Bazant, Laurent Demanet, Ronald Garcia Ruiz, Bradley Olsen, Cristina Rea, and Gunther Roland among others.
Executive Summary
MIT researchers are set to contribute to the U.S. Department of Energy’s (DOE) Genesis Mission through 15 collaborative projects selected for funding in Phase I. The Genesis Mission aims to build a "world’s most powerful integrated science discovery platform" by fostering cross-sector collaborations utilizing AI, supercomputing, quantum systems, and advanced instruments to accelerate breakthroughs in energy, scientific discovery, and national security.
The initial phase focuses on demonstrating research workflows that integrate AI with scientific investigation and rigorously evaluating the scientific merit of approaches. Projects under the mission are collaborative, requiring expertise from academia, industry, and/or national laboratories. Some specific project areas include developing quantum sensors for fundamental physics questions, advancing chemical-free methods for rare earth element extraction, modeling plasma behavior in fusion reactors, and designing materials using self-assembling biomolecules.
Six of the selected Phase I projects are led by MIT principal investigators, including those focused on AI-driven discovery methods, quantum sensing, inverse design, and multi-modal foundation models. The remaining nine involve collaborations with institutions like Argonne National Laboratory, Texas A&M University, Purdue University, and LBNL.
Full Take
The structure of the Genesis Mission points toward a strategic effort to operationalize high-level theoretical concepts—like leveraging AI and quantum systems for discovery—into tangible, actionable research workflows across diverse sectors. The emphasis on integrating AI with fundamental science and engineering suggests an implicit assumption that bottlenecks in scientific advancement are methodological, not purely physical or computational.
The distribution of leadership among MIT researchers and external partners highlights a distributed approach to complex problem-solving. The pattern involves layering expertise: foundational physics/chemistry knowledge is being augmented by cutting-edge AI/machine learning techniques and large-scale systems simulation (digital twins). This suggests an underlying paradigm where transformative capability relies on synthesizing disparate domains rather than deep specialization within a single silo.
The focus on identifying "promising pathways toward transformative capabilities at scale" for subsequent funding implies a gatekeeping mechanism: success is measured not just by the novelty of individual discoveries but by the demonstrated efficacy of cross-disciplinary workflows capable of accelerating discovery across energy, materials science, and national security priorities. The necessity for teams to demonstrate integrated workflows suggests that the true innovation lies in the connective tissue between AI, quantum mechanics, and experimental physics.
Bridge questions: If the primary goal is workflow integration, how should metrics be developed to fairly value the synergistic outputs of distinct disciplinary contributions? What are the inherent risks when incentivizing rapid, cross-sectoral collaboration under a mandate focused on national priorities? Does this framework risk prioritizing scalable technological implementation over purely foundational scientific inquiry?
Sentinel — Human
The text appears to be a standard, professionally written press release detailing a scientific funding initiative, exhibiting the clear structure and factual density expected from official announcements.