Accelerating Intelligent Materials & Manufacturing Systems (AIM₂S)

U.S. Department of Energy (DOE), Advanced Materials and Manufacturing Technologies Office (AMMTO) in collaboration with Partnership Intermediary, EnergyWerx announces the Accelerating Intelligent Materials & Manufacturing Systems (AIM₂S) Funding Opportunity. This Opportunity is focused on integrating artificial intelligence (AI) technologies to address bottlenecks in the U.S. energy manufacturing industrial base, including critical-materials supply chains, materials qualification, product quality, throughput, process consistency, cost, and adoption of new materials and processes.  The Opportunity targets production-relevant validation and measurable improvements across energy manufacturing.

Background

In November 2025, the U.S. Department of Energy launched the Genesis Mission, a national initiative aimed at transforming scientific discovery and engineering productivity through AI and advanced computing. Advanced materials and manufacturing are among the 26 national challenge areas, where AI-enabled innovation can accelerate American leadership, strengthen domestic supply chains, and improve the transition of promising technologies from research to deployment.  

Despite significant advances in materials discovery, process modeling, automation, and manufacturing data infrastructure, promising technologies often encounter substantial barriers when transitioning from laboratory-scale validation to production-relevant environments. These barriers are particularly acute in energy-relevant manufacturing sectors characterized by complex system integration, stringent performance requirements, sensitive technical data, and the need for reliable qualification pathways.  

The development and testing of new AI technologies that advance both materials innovation and intelligent manufacturing systems relevant to U.S. energy technologies will be prioritized for applications with a clear pathway to high-impact energy products, including but not limited to batteries, grid components (e.g., conductors and transformers), magnets, and high-performance components for aggressive operating environments, and other advanced materials and manufacturing applications that support domestic energy manufacturing capacity.  

Because AI-enabled materials and manufacturing systems may involve sensitive technical data, proprietary process information, connected equipment, and operational technology environments, projects should incorporate appropriate risk-based cybersecurity, data governance, and access-control practices commensurate with the project scope and deployment context. These needs establish the basis for AIM₂S as a targeted to advance AI-enabled materials and manufacturing technologies toward production-relevant validation.

The U.S. energy manufacturing sector faces persistent, system-level challenges that limit the speed, scalability, quality, cost-effectiveness, and resilience of domestic production. These challenges (listed in Table 1) include limited production throughput, high defect rates, inconsistent product quality, high manufacturing costs, long materials qualification timelines, challenging supply chains, limited scalability of manufacturing processes, and fragmented data across materials, processes, equipment, and supply chains. To address these challenges, there is a critical need to leverage data-driven approaches, artificial intelligence (AI), adaptive control, and system-level optimization to improve performance, reliability, and scalability of energy-relevant materials and manufacturing systems.

AIM₂S will complement broader DOE AI efforts by focusing on AI-enabled materials and manufacturing technologies that bridge the gap between foundational research and production-relevant validation. The effort will emphasize materials qualification, manufacturing process integration, equipment-level automation, and system-level optimization for energy-relevant applications. It aims to accelerate the integration of advanced materials into energy systems by utilizing AI technologies, focusing on high-performance materials for power generation, grid transmission, and energy storage. For power generation applications, areas of interest include those utilizing nuclear, fossil, geothermal, and hydropower energy sources.  

Projects are expected to advance technologies from Technology Readiness Level (TRL) 3 and Manufacturing Readiness Level (MRL) 4 toward TRL 6 and MRL 7, helping bridge the gap between foundational research and pilot-scale demonstration. Performer projects should define the starting maturity level, target maturity level, technical basis for advancement, and quantitative evidence needed to demonstrate progress.

The Opportunity is organized around two complementary objectives:

1. AI-enabled Materials Discovery, Qualification, and Integration:
   The first objective is to bridge the gap between materials discovery and deployment by developing and demonstrating AI-enabled frameworks for rapid and reliable materials qualification and integration. Areas of particular interest include, but are not limited to, advanced materials relevant to energy applications, such as grid infrastructure, energy storage, and power generation systems, including gas, nuclear, hydropower, geothermal and coal. The effort targets promising mid-stage technologies defined as post-laboratory demonstration (TRL 3), with an emphasis on eventual subsystem- or full-system-level demonstration and deployment (TRL 6). Key technical solutions include utilizing physics-informed machine learning models, AI-driven digital twins, automated data fusion, uncertainty-aware models, inverse design, and high-throughput computational or experimental workflows to support certification and risk-informed decision-making.
   
   The emphasis is on demonstrating measurable progress toward a specific material, component, process, or application-relevant outcome, rather than developing AI models in isolation. Projects should identify a clear materials-related bottleneck, define the role of AI in addressing that bottleneck, and provide quantitative metrics to evaluate progress.
   ‍
2. Intelligent AI Manufacturing Systems:
   The second objective is to develop, integrate, and validate AI-enabled manufacturing technologies that improve equipment, process sequences, automation, production systems, and manufacturing workflows at system-level performance under operationally relevant conditions. This involves demonstrating integrated, digital-first qualification workflows across materials, processes, and systems toward pilot-scale, production-relevant environments. Projects may include real-time monitoring, adaptive process control, predictive optimization, sensor fusion, equipment-level automation, AI-enabled digital twins, machine-to-machine coordination, and system-level production optimization.
   
   The emphasis is on commercially transformative improvement in manufacturing performance, such as throughput, quality, yield, reliability, cost, scrap reduction, process stability, or readiness for integration into pilot-scale production environments. Projects may focus on targeted manufacturing stages, unit operations, equipment systems, or integrated manufacturing workflows, provided there is a clear pathway toward production-relevant validation.

The expectation is to advance materials development approaches and/or manufacturing processes through AI-enabled approaches. This activity focuses on mid-stage technology maturation, TRL 3–6 and/or MRL 4-7, bridging foundational research and early-stage validation through lab-scale or production-relevant demonstration. By supporting early-stage innovation, the program seeks to establish a pipeline of AI-enabled technologies that can be advanced toward pilot-level production or deployment within approximately 24 months.

Expected outcomes include a quantifiable reduction in materials qualification and certification timelines (30-50% decrease), validated digital qualification frameworks demonstrated with industry partners, successful insertion of priority materials into pilot-scale manufacturing systems (2x acceleration), and transformative improvements in manufacturing performance, including throughput (2x improvement), quality (20-40% improvement), yield (20-40% improvement), reliability (50% improvement), cost (25-50% reduction), scrap reduction (20-40% reduction), process stability (50% improvement), and readiness for pilot-scale production or deployment. Expected outcomes also include clearly defined commercialization pathways to enhance domestic supply chain resilience. Each performer project should define a specific material, component, process, equipment system, workflow, or application-relevant end goal and demonstrate measurable progress toward that end goal using quantitative technical, manufacturing, and/or commercialization metrics.

Teaming Partner Participation Details

Technology Developers are recommended to have an Industrial Host Site. To facilitate connections between Technology Developers and potential Host Sites, EnergyWerx is compiling a Teaming Partner List^[1] for this Opportunity. The Teaming Partner List allows entities that may wish to participate in a project to express their interest to other applicants and explore potential partnerships.

• Sign up for the Teaming Partner List: Click Here to Sign Up
  
  • NOTIFICATION: Upon submission, applicants will receive an email containing the report and password, which will allow them to view new teaming partners who have provided their organization's information.

[1] DISCLAIMER: By submitting a request to be included on the Teaming Partner List, the requesting organization consents to the publication of the referenced information. By facilitating the Teaming Partner List, DOE is not endorsing, sponsoring, or otherwise evaluating the qualifications of the individuals and organizations that are identifying themselves for placement on this Teaming Partner List. DOE will not pay for the provision of any information, nor will it compensate any applicants or requesting organizations for the development of such information.

• ‍Submission Window: November _, 2026 - March _, 2027‍
  
  • ‍Start the AIM₂S Application (Submissions are due March _, 2027 at 3:00 PM ET)
  • Be sure to have all the appropriate project information and supporting documentation needed for the application.
    
• Letter of Intent due January 13, 2027 at 3:00 PM ET
  
  • Submit your Letter of Intent

Applicants must submit the following documents:

• Letter of Intent (optional)
• A cover page outlining the approach to testing and validation, details on the intended testing campaign, and the expected outcomes of the project.
• A technical volume up to ten pages that clearly:
  
  • Addresses one of the key objectives defined in the review criteria and project technical volume template.  
  • Provides previous testing or production data reports, meant to demonstrate the novelty and technical readiness in the strongest format and detail able.  
  • Provides proof (or credible plan) to be able to address critical bottlenecks in the U.S. energy manufacturing industrial base, including critical-materials supply chains, materials qualification, product quality, throughput, process consistency, cost, and adoption of new materials and processes. 
• A workplan up to five pages that clearly:
  
  • Defines and describes the major tasks to be performed by the project team throughout the course of the project, including identifying milestones, during and budget for each task.
  • Provides monthly milestones that demonstrate a technical achievement (e.g., major outcome or deliverable).
    
    • One milestone per quarter (one every three months) should be identified as a major project milestone.
  • Demonstrates cost share.
• A letter of support from the industrial partner, and any other company, agency, or other party that has ownership/rights to any proposed feedstock materials or technology, where applicable. ‍
  
  • ‍There is no limit on the total number of letters of support.
• Save all materials as pdfs following the following naming conventions:
  
  • Cover page (1 page): LeadOrganizationName_CP  
  • Technical Volume (max 6 pages): LeadOrganizationName_TV
  • Work Plan (max 5 pages): LeadOrganizationName_WP
  • Letter(s) of support (Submitted as one file, each letter 1 page, no limit on number of letters): LeadOrganizationName_LS

The AIM₂S Opportunity launched on [MONTH] [DATE], 2026. Please click here for the full announcement article for more information.

Informational Webinars: DOE will present and summarize the details of the AIM₂S Opportunity. Potential applicants will gain a better understanding of the Opportunity's scope, scale, and intent and have the opportunity to ask questions of DOE staff.

• Register for the Informational Webinar on [MONTH] [DATE], 2026 at 3:00 PM ET
  
  • Focuses predominantly on explaining the Opportunity with some time for questions at the end.
• Register for the Informational Webinar on [MONTH] [DATE], 2027 at 3:00 PM ET
  
  • Focuses predominantly on explaining the Opportunity with some time for questions at the end.‍
• Register for the Informational Webinar on [MONTH] [DATE], 2027 at 3:00 PM ET‍
  
  • Focuses predominantly on explaining the Opportunity with some time for questions at the end.

For an optimal experience, we recommend downloading the Microsoft Teams software to your computer, smartphone, or tablet.

The anticipated program schedule and funding structure are:

• Maximum period of performance: Up to 24 months.
• Funding: Up to $20 million total; expected individual performer awards of approximately $1.1 million to $2.2 million.

Detailed scope, deliverables, performance metrics, and milestones will be finalized in each performer's Business-to-Business (B2B) agreement.

• ‍Submission Window: November _, 2026 - March _, 2027‍
  
  • ‍Start the AIM₂S Application (Submissions are due March _, 2027 at 3:00 PM ET)
  • Be sure to have all the appropriate project information and supporting documentation needed for the application.
    
• Letter of Intent due January 13, 2027 at 3:00 PM ET
  
  • Submit your Letter of Intent

Applicants must submit the following documents:

• Letter of Intent (optional)
• A cover page outlining the approach to testing and validation, details on the intended testing campaign, and the expected outcomes of the project.
• A technical volume up to ten pages that clearly:
  
  • Addresses one of the key objectives defined in the review criteria and project technical volume template.  
  • Provides previous testing or production data reports, meant to demonstrate the novelty and technical readiness in the strongest format and detail able.  
  • Provides proof (or credible plan) to be able to address critical bottlenecks in the U.S. energy manufacturing industrial base, including critical-materials supply chains, materials qualification, product quality, throughput, process consistency, cost, and adoption of new materials and processes. 
• A workplan up to five pages that clearly:
  
  • Defines and describes the major tasks to be performed by the project team throughout the course of the project, including identifying milestones, during and budget for each task.
  • Provides monthly milestones that demonstrate a technical achievement (e.g., major outcome or deliverable).
    
    • One milestone per quarter (one every three months) should be identified as a major project milestone.
  • Demonstrates cost share.
• A letter of support from the industrial partner, and any other company, agency, or other party that has ownership/rights to any proposed feedstock materials or technology, where applicable. ‍
  
  • ‍There is no limit on the total number of letters of support.
• Save all materials as pdfs following the following naming conventions:
  
  • Cover page (1 page): LeadOrganizationName_CP  
  • Technical Volume (max 6 pages): LeadOrganizationName_TV
  • Work Plan (max 5 pages): LeadOrganizationName_WP
  • Letter(s) of support (Submitted as one file, each letter 1 page, no limit on number of letters): LeadOrganizationName_LS

Eligbility Criteria

We encourage participation by a broad range of performers, including non-traditional organizations, small businesses, startups, universities, national laboratories, nonprofit entities, and industry teams. This is designed to advance high-impact solutions without requiring traditional large-scale internal computing infrastructure to achieve sufficient technical capability or access to relevant data and validation environments. Please note that while national labs may participate, they are not eligible to be the prime and federal share is limited to 10% (in aggregate) to the total project cost.

Eligible Applicants

• Domestic Entities:
  
  • Domestic entities are eligible to apply as prime recipients or subrecipients. The following types of domestic entities are eligible to participate as a prime recipient or subrecipient of this Opportunity:
    
    • Institutions of higher education;
    • For-profit entities;
    • Nonprofit entities;
    • State and local governmental entities; and
    • Indian Tribes, as defined in section 4 of the Indian Self-Determination and Education Assistance Act, 25 U.S.C. § 530424²
  • To qualify as a domestic entity, the entity must be organized, chartered, or incorporated (or otherwise formed) under the laws of a particular state or territory of the United States; have majority domestic ownership and control; and have a physical place of business in the United States.
  • DOE/NNSA FFRDCs are eligible to apply for funding as a prime recipient or subrecipient.
  • Non-DOE/NNSA FFRDCs are eligible to participate as a subrecipient but are not eligible to apply as a prime recipient.
  • Federal agencies and instrumentalities (other than DOE) are eligible to participate as a subrecipient but are not eligible to apply as a prime recipient.
  • Entities banned from doing business with the U.S. government such as entities debarred, suspended, or otherwise excluded from or ineligible for participating in federal programs are not eligible.
• Foreign Entities
  
  • Foreign entities are not eligible to apply as either a prime recipient or subrecipient. Applicant must certify that it is not owned by, controlled by, or subject to the jurisdiction or direction of the government of the Country of Risk.
  • Persons participating in a Foreign Government-Sponsored Talent Recruitment Program of a Foreign Country of Risk are prohibited from participating in projects selected for federal funding under this Opportunity. Should an award result from this Opportunity, the recipient must exercise ongoing due diligence to reasonably ensure that no individuals participating on the DOE-funded project are participating in a Foreign Government-Sponsored Talent Recruitment Program of a Foreign Country of Risk. Consequences for violations of this prohibition will be determined according to applicable law, regulations, and policy. Further, the recipient must notify DOE within five (5) business days upon learning that an individual on the project team is or is believed to be participating in a foreign government talent recruitment program of a foreign country of risk. DOE may modify and add requirements related to this prohibition to the extent required by law.
  • DOE retains the prerogative to require additional information from the applicants to verify the applicant meets the eligibility requirements.

Artificial Intelligence (AI) Application Use

• The project summary must indicate the extent to which, if any, generative artificial intelligence (AI) technology was used to generate, develop, edit application and/or proposal.  
• All submissions to DOE are subject to information and disclosure statutes and regulations, including the Freedom of Information Act, Privacy Act, and 10 C.F.R. § 1004.11.  Applicants are responsible for the accuracy, authenticity, and authorship represented in your proposal submission for merit review, including content developed with the help of generative AI tools.  Applicants must ensure that the team is fully capable of performing the work described in the application and that the submission of the application does not and will not infringe or violate any rights of any third party or entity.
• Applicants should be aware that using generative AI may introduce significant risks, including, but not limited to, research misconduct resulting from fabrication, falsification, or plagiarism when proposing, performing, or reviewing research, or in reporting research results.  Federal regulations governing procedures for handling research misconduct allegations concerning research supported by DOE grants, cooperative agreements, and management and operations (M&O) contracts, are specified in 10 C.F.R. § 733.  Specific provisions governing research misconduct procedures for financial assistance recipients (under grants and cooperative agreements) are specified in 2 C.F.R. § 910.132.

Review Criteria

AMMTO will determine whether the team has sufficiently:

1. Proposed AI-enabled materials or manufacturing technology at approximately TRL 3–6 and/or MRL 4-7 at the time of application;  
2. Provided evidence that the proposed AI approach has a credible technical basis, such as preliminary data, prototype models, published research, prior demonstrations, relevant software tools, domain expertise, or access to appropriate datasets;  
3. A clearly defined energy-relevant material, process, equipment, system, qualification workflow, or manufacturing outcome of commercial importance that the AI-enabled approach is intended to advance;  
4. A credible plan to validate performance using quantitative metrics;  
5. Access to the personnel, data, equipment, facilities, partners, or validation environments needed to execute the proposed work;  
6. A pathway for advancing the technology toward pilot-scale demonstration, commercial adoption, or integration into domestic energy manufacturing supply chains; and  
7. Appropriate consideration of cybersecurity, data governance, access control, and protection of sensitive technical information, as applicable to the project scope.

This will be graded by reviewing the following 3 criteria sections:

Criteria 1: Technical Description, Innovation, and Readiness

Assess the novelty, technical approach, maturity, and commercial viability of the proposed technology; such as:

• Is the technology being developed novel in concept or application?
• Does the proposed AI-enabled approach represent a meaningful technical advance over existing methods, either through AI innovation, novel integration, or demonstrated improvements in materials or manufacturing performance?
• Are baseline data and proof provided to verify starting readiness (TRL 3–6 / MRL 4-7)?
• Will the technology address a critical bottleneck in the domestic energy manufacturing supply chain?
• Has the applicant defined clear, quantitative metrics to measure progress against existing benchmarks?
• Does the technology have the potential to achieve cost parity or competitive advantage and at what scale?
• Is there a clear value proposition and a viable path to market adoption?  
• Does the proposal demonstrate a credible economic value proposition, including potential cost savings, productivity improvements, or other benefits that could support future commercialization?
• Does the proposal provide a credible validation strategy, including appropriate baseline comparisons, representative datasets, uncertainty quantification, and measurable performance targets?

Specific to Topic Area 1:

• Will the proposed materials resolve the bottleneck?
• Is the role of AI for addressing the bottleneck well defined?

Specific to Topic Area 2:

• Will the technology operate in relevant working conditions?
• Are advanced manufacturing controls (e.g., real-time monitoring, adaptive control, sensor fusion, equipment automation) sufficiently defined to improve system-level performance?
• Does the proposal establish quantifiable targets for transformative manufacturing improvements (e.g., throughput, yield, scrap reduction, quality, or process stability)?
• Can the technology be readily integrated into ongoing equipment, unit operations, or existing manufacturing workflows?

Criteria 2: Workplan, Milestones, and Project Management

Assess the completeness, logic, feasibility, and risk management of the proposed project approach; such as:

• Is the work breakdown structure logically sequenced with clearly defined tasks and dependencies?
• Are the proposed timelines, level of effort, and task budgets realistic and reasonable?
• Does the workplan include clearly defined monthly technical milestones, with major quarterly SMART milestones that demonstrate measurable progress toward project objectives?
• Are Go/No-Go decision points defined with quantifiable, verifiable pass/fail metrics?
• Is the validation plan and design of experiments adequate to demonstrate target operational maturity?
• Does the plan include a realistic risk matrix with actionable mitigation strategies?
• Are data management, cybersecurity protocols, and quality controls sufficiently addressed?
• Does the workplan demonstrate a credible progression from the current TRL/MRL to the proposed target maturity level, supported by measurable technical achievements and validation under increasingly representative operating conditions?

Criteria 3: Technical Qualifications and Resources

Assess the capabilities, track record, facilities, and partner commitments of the project team; such as:

• Does the team have demonstrated experience executing projects of similar technical complexity?
• Is the time commitment of the Principal Investigator and other key personnel sufficient to drive execution?
• Does the team have demonstrated access, or a credible and documented plan to secure timely access, to the facilities, equipment, computing resources, and data necessary for project execution?
• Has the team secured access to the required training data and intellectual property?
• Are key partners and industrial collaborators meaningfully integrated into the workplan?
• Does the proposal demonstrate strong industrial pull, partner cost share, and support letters?
• Has the team identified skill gaps and provided credible plans to address them through hiring or contracting?
• Does the team possess the capabilities to drive commercialization beyond this project?
• Does the project team include appropriate materials/manufacturing domain expertise, AI expertise, and access to industry-relevant validation environments necessary to demonstrate the proposed technology?

Review Scoring

Reviewers will grade each project’s Criteria section from 0 - 10 using the following scale:

• Score 9–10 (Superior / Low Execution Risk): The proposal exceeds requirements.
  
  • Examples include: Every task has definitive inputs, outputs, verification methods, and measurable metrics including those related to facilities, data access, and key personnel commitments are fully secured. Go/No-Go gates contain strict quantitative boundaries. ‍
• Score 7–8 (Good / Acceptable Execution Risk):  
  
  • Examples include: The proposal meets all requirements with credible execution planning, while minor details missing in secondary milestone verification or risk mitigation, but core delivery path, experimental pathways, and key personnel are solid. ‍
• Score 5–6 (Adequate / Moderate Execution Risk): There is merit but more details required.  
  
  • Examples include: The technical concept is viable, but the execution plan relies on generalized tasks without rigorous SMART metrics, or validation protocols lack statistical depth, or access to critical validation facilities/data remains unconfirmed. ‍
• Score 3–4 (Poor / High Execution Risk): Substantial deficiencies in the criteria.
  
  • Examples include: significant ambiguity regarding who performs tasks, how models are trained and validated, how Go/No-Go decisions are enforced, or superficial partnerships. ‍
• Score 1–2 (Deficient / High Failure Risk): Critical issues across all areas.  
  
  • Examples include: unrealistic claims without preliminary data or empirical foundation, execution plan lacking structure, critical path logic, or verifiable metrics, as well as inadequate personnel or facilities to accomplish the proposed scope.

Each Criteria will have the following weight to establish a final score (0 - 10) for the proposal:

• Criteria 1: Technical Description, Innovation, and Readiness : 40%
• Criteria 2: Workplan, Milestones, and Project Management: 45%
• Criteria 3: Technical Qualifications and Resources: 15%

Additionally, DOE reserves the right to require additional or clarifying information regarding the application submissions, the team, and any other matters related to the anticipated funding. If DOE contacts an applicant for additional or clarifying information, it does not signify that the applicant has been selected for negotiation of award or that the applicant is among the top ranked applications. Applicants selected for negotiation will be required to agree to standard DOE funding legal terms and conditions.