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Advanced Materials and Manufacturing Technologies (AMMT) 2025 Roadmap
The mission of the Advanced Materials and Manufacturing Technologies (AMMT) program is to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies in support of the United States (US) leadership in a broad range of nuclear energy applications. The vision of the AMMT program is the expansion of reliable and economical nuclear energy enabled by advanced materials and manufacturing technologies. Four major goals were set to realize the mission and vision of the AMMT program, including: (1) develop advanced materials and manufacturing technologies that have cross-reactor applications, (2) establish and demonstrate a rapid qualification framework that supports diverse materials and manufacturing technology needs, (3) evaluate materials performance in a range of nuclear environments, and (4) accelerate commercialization of new technologies through technology maturation. The program is designed to deliver solutions that support a wide range of reactor technologies.
36 MATERIALS SCIENCE↗
Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts
The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.
36 MATERIALS SCIENCE↗
Assessment of Microstructure Prediction Capabilities for Powder Bed Fusion Stainless Steel 316
The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the characteristic process-structure-property relationships of additive manufacturing (AM) materials pose challenges for the qualification and certification of AM nuclear components. In particular, component-scale variations in microstructure and properties can be driven by localized changes in melt pool dynamics due to how process parameters interact with different part geometries. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents final results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing identified challenges and future opportunities for connecting the developed simulation workflow with mechanics simulations for prediction of part performance.
36 MATERIALS SCIENCE↗
UV Degradation in Backsheets: A Ray-Tracing Irradiance Simulation Approach
Around 90% of current photovoltaic (PV) modules are less than ten years old. New PV technologies and materials are deployed without documented durability and performance histories. Accelerated testing attempts to capture degradation modes but can produce false results with such rapid deployment of new materials. Current testing assumes UV dosage on the rear of a module to be 10% of that incident on the front. We present a method to quantify degradation on PV backsheets in the field. We aim to evaluate if current acceleration factors for UV damage in chambers are properly estimating degradation for different PV site installations. The method leverages bifacial_radiance to ray-trace and evaluate irradiance on the front and the rear of the modules. Then an equation to estimate the relative degradation is proposed.
accelerated testing↗
Summary of Methodology for Mitigating Risks Associated with Licensing and Qualifying AM Nuclear Materials
The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program focuses on accelerating the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. Laser powder bed fusion (LPBF) is one of the most popular additive manufacturing (AM) processes for fabricating components with intrinsically complex geometries. LPBF was extensively explored for nuclear applications under the previous Transformational Challenge Reactor program. Additionally, Oak Ridge National Laboratory developed and licensed the Peregrine software and larger digital platform that couples machine learning and in situ data collection during AM to detect anomalies and any evolved defects. The digital platform will be critical to (1) the qualification of AM components for nuclear applications that link location-specific data to macroscopic properties and (2) predict final component performance. Current in situ process monitoring tools are valuable for observing the formation of stochastic flaws, but additional data are needed to predict the resulting microstructures and associated material performance. Rapid cooling rates and large thermal gradients have caused large heterogeneities in the microstructure, which cause anisotropy in mechanical performance. The AMMT program is evaluating the best approaches for addressing these heterogeneities and their effect on component performance using a combination of multiscale modeling, enhanced in situ process monitoring, and high throughput experimental testing. This report summarizes strategies for mitigating the risks associated with qualifying AM components, including developing new sensing capabilities for in situ process monitoring and characterizing melt pool solidification and residual stresses to inform multiscale modeling efforts.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts
The Advanced Materials and Manufacturing Technologies (AMMT) program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy However, the unique aspects of additive manufacturing (AM) materials in terms of their processing history, microstructure, and properties, are a major barrier for qualification and certification of nuclear components. Much of this challenge may be attributed to component scale variations in microstructure and properties that are driven by local influences of process conditions and geometry on thermal history, melt pool dynamics, and corresponding microstructure evolution. Computational modeling tools may be helpful in this regard to aid in predicting and controlling this level of variability. The purpose of this report is to review the current state-of-the-art for process modeling with regards to metal AM. For this purpose, we consider specifically the case study of laser powder bed fusion (LPBF) processing of SS316, a family of alloys that are both commonly used in nuclear energy applications and suitable for AM processing. The report first introduces the necessary components of a process modeling workflow, followed by a review of the current status of each. At the end, application of these modeling tools to understanding variability in AM process given their current state are considered, and recommendations for future development are proposed
36 MATERIALS SCIENCE↗
Active learning path-dependent properties using a cloud-based materials acceleration platform
Solid state materials are central to many modern technologies in which a given material may be exposed to a variety of environments. The material properties often vary with the sequence of environments in an irreversible manner, resulting in a quintessential path-dependency in experimental observables. While sequential learning techniques have been effectively deployed for accelerating learning of state properties of materials, they often use a consistent environment path in all experiments. To elevate such techniques for making optimal decisions in experimental investigations of path-dependent properties, we introduce an iterated expected information gain acquisition function that optimizes over entire experimental trajectories. This approach is implemented within a cloud-based Materials Acceleration Platform architecture utilizing an event-driven stateful broker coupled with remote HELAO (Hierarchical Experimental Laboratory Automation and Orchestration) instances and an AI science manager. The platform's efficacy was demonstrated through a case study optimizing multi-step spectro-electrochemical experiments to identify optically stable potential windows in (Co–Ni–Sb)O z metal oxides. The system successfully integrated AI-driven experiment design, remote laboratory automation, and cloud-based data infrastructure, validating the platform's capability for managing complex, adaptive, path-dependent workflows in materials discovery.
Guevarra, Dan [California Institute of Technology ↗
Magnify: A Final Technical Report of the American Energy and Manufacturing Competitiveness Partnership
The energy landscape has undergone profound transformation, with dramatic shifts having an impact on U.S. productivity, global investment, manufacturing operations, and job creation. The sense of urgency for a tighter linkage between clean technologies, energy and advanced manufacturing has only grown. Prior to 2009, the tone of the nation’s energy conversation was centered on how to deal with long-standing energy security challenges and scarcity. Today, the tone is focused on seizing emerging energy growth opportunities to transform America’s industrial base and job creation outlook—centering on energy abundance and strength. In this context, the Council on Competitiveness and the Department of Energy’s Office of Energy Efficiency & Renewable Energy (EERE) teamed in the American Energy & Manufacturing Competitiveness (AEMC) Partnership to tackle two major goals via a multi-year partnership. The AEMC Partnership identified means to: • Increase U.S. competitiveness in the production of clean energy products • Increase U.S. manufacturing competitiveness across the board by increasing energy productivity The AEMC Partnership has engaged hundreds of leaders from industry, academia, labor and government in a series of 9 regional, progressive dialogues; original research; and 4 national summits. The AEMC dialogues and summits spanned the United States—taking place in our nation’s greatest manufacturing, research, technology and innovation hotspots. The goals of the AEMC Partnership have been straightforward: • State and define key barriers, challenges, and problems in U.S. competitiveness in manufacturing of clean energy products, energy efficiency products, and advanced manufacturing products. • Dive deeply into these problems and generate policies, solutions, concepts and models where the U.S. public and private sectors can work together to solve these problems. • Catalyze policy solutions—including models for public-private partnership (PPP) pilots—to increase competitive manufacturing of clean energy and energy efficiency products in the USA. • Elevate and increase awareness of the importance and benefits of competitive clean energy manufacturing. • Understand how energy game-changers, like breakthrough technologies, impact U.S. clean energy and energy efficient manufacturing. The Council on Competitiveness worked with its stakeholder network to generate potential PPP concepts and proposals to advance the goals of the AEMC Partnership. Magnify outlines 2 PPP concepts—honed by dialogues, conversations, interviews and research—that could be carried out by EERE and/or the Council to increase the competitive production of clean energy products, energy efficient products, and advanced manufacturing in the USA. Magnify’s 2 PPP concepts aim to bridge very specific gaps in the nation’s innovation ecosystem: • Clean Energy Materials Accelerator: This PPP concept focuses on reducing the risks associated with deploying newly developed materials in commercial products and processes by creating a platform to identify and address common challenges; increasing access to existing materials qualification and characterization tools; and creating standards for advanced materials with leaders in industry, academic, government, and other organizations. Why accelerate materials production? As the AEMC Partnership Dialogue and supporting research from the public and private sectors have documented, countries that lead in making next-generation materials will gain significant competitive advantage by unleashing a new wave of manufacturing innovation. • Manufacturing and Energy Technology Accelerator: This PPP concept is a new, physical and virtual collaborative resource platform designed to connect the nation’s world-class innovation institutions—SMEs, large multinational companies, universities, national laboratories, etc.—to facilitate the transition of cutting-edge clean energy technologies into products, processes, or services that are manufactured in the United States. Why should public and private sector leaders in innovation partner to co-create a scale-up platform? The United States is already a mecca for the world’s greatest minds in science and technology—drawn to our shores by world-class universities and opportunities to work with global leaders in innovation. Unfortunately, when it comes time to bring their ideas to market, technologists and entrepreneurs often choose, or are forced, to locate manufacturing overseas. The United States must regain its position in the world as a national scale-up platform for next-generation technologies. A thorough explanation of these PPP concepts and the rationale behind these recommendations is provided in Part 3 of Magnify. Magnify is an important step on the critical journey to define barriers, challenges and problems in the manufacturing of clean energy products and energy efficient products—and further honing concepts for scalable, public-private partnerships—to increase the competitive manufacturing of clean energy and energy efficient products, and the energy productivity throughout the U.S. manufactur¬ing sector. The rest of the world is waking up to the opportunities associated with a strategic focus on manufacturing and energy competitiveness. Global competition is on the rise, and the stakes are high for the United States to act now, to act decisively, and to leverage inherent strengths to ensure a more prosperous, competitive future for decades to come.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Roadmap for the Application of Ion Beam Technologies to Challenges for the Advancement and Implementation of Nuclear Energy Technologies
The use of ion beams in interrogating materials performance for nuclear energy applications is advancing at a rapid rate. Ion irradiations have been proven to produce radiation effects data that are of direct relevance for understanding neutron-induced displacement damage. This development reflects significant and continued investment in infrastructure by the U.S. Department of Energy (DOE) as well as support of science and engineering research at universities and national laboratories. Application of ion beam irradiation methods to enhance the deployment of materials in reactors is in the spirit of the Gateway for Accelerated Innovation in Nuclear (GAIN) initiative sponsored by DOE to accelerate the deployment of innovation in the U.S. nuclear industry. Ion beam irradiation shows considerable promise for assisting in down selecting candidate materials for use in both current and advanced nuclear energy systems. Ion beams allow rapid achievement of materials damage levels not accessible by neutron irradiation in test reactors due to cost and time constraints. Ion beam studies provide data on the effects of irradiation under very specific conditions of temperature, radiation dose, and radiation dose rate that are difficult or impossible to achieve via reactor irradiation experiments. This data can be used to develop and validate detailed predictive radiation effects models for use in reactor materials research and development. Despite the current high state of the art and the maturity of quality assurance, there are still challenges to the deployment of ion beam data in support of reactor materials qualification. For the most part, these challenges arise from the lack of a detailed mechanistic understanding of potential difference between ion induced and neutron induced materials damage, which is exacerbated by difficulties in obtaining bulk physical and mechanical properties data from the micron-scale heavy ion irradiation depths. In particular, improved understanding of dose rate effects, as a function of temperature, are needed. This document offers a roadmap for the development and enhancement of current U.S. ion beam irradiation technologies within university and national laboratory settings, and especially for the deployment of new highly controlled in situ interrogation of materials during irradiation to provide dynamic and mechanistic data for model development. The status and capabilities of relevant U.S. ion beam facilities are summarized and recommended “best practices” for performing ion irradiations are described. The potential role of ion beam irradiations to assist the development and deployment of reactor materials is outlined. Key principles include developing methods for rapid and cost-effective materials selection and development, characterizing fundamental material response under irradiation, and developing a robust mechanistic understanding of microstructure evolution under irradiation (including development and validation of reliable predictive models for microstructure evolution).
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing
The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Promoting regulatory acceptance of combined ion and neutron irradiation testing of nuclear reactor materials: Modeling and software considerations
As the needs for the nuclear energy industry continue to evolve in the 21st century, timely adoption of new technological solutions acceptable to regulatory agencies is critical. Quantitative prediction of radiation damage in materials and its impact on mechanical properties is a key component of licensing and regulatory decisions regarding nuclear power plants. Accelerated testing methodologies such as combined ion and neutron irradiation data sets are crucial for the development and deployment of new materials and new manufacturing methods (e.g., additive manufacturing). However, regulatory acceptance of accelerated testing methodologies is necessary for their adoption. Further, the present work discusses the fundamental basis for comparing ion- and neutron-induced material microstructures, the theory behind interpreting radiation damage across length and time scales and radiation types, and the codes, standards, and quality assurance concerns surrounding different modeling methods and software. In particular, recommendations are given as to the path forward that will enable national laboratories, academia, and industry to develop the modeling and software basis for regulatory acceptance of the combined use of ion and neutron irradiation for material performance evaluation.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Cost‐Responsive Optimization of Nickel Nanoparticle Synthesis
Abstract Early‐stage cost evaluation during catalyst development holds the potential to accelerate the commercialization and deployment of advanced catalytic materials for sustainable chemical processes. The modeling and assessment of manufacturing costs as early as the laboratory synthesis scale, for example, focusing on materials costs and synthesis performance metrics, can support the development of an experimental–economic feedback loop that enables rapid insight into cost drivers associated with catalyst synthesis and highlights areas that require focused research and development effort. Ultimately, this feedback loop supports the realization of an economic understanding of the overall synthetic process and highlights opportunities to reduce costs, serving as the foundation for the scale‐up of catalyst manufacturing. Herein, a case study is presented utilizing CatCost, a free and publicly available estimation tool for the evaluation of catalyst manufacturing costs, to perform a cost‐responsive optimization of the synthesis of nickel nanoparticles (Ni NPs). It is demonstrated that reagent substitutions with more cost‐effective analogs, coupled with stoichiometric optimization, afford a 58% reduction in raw materials cost without changing the product yield or properties.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Report on the Update and Codification of Expert Knowledge into ASTM Standard Practice E521
The overall purpose of the Grand Challenge Integrated Research Project (IRP), Accelerating the Qualification of Materials to Enable Rapid Deployment of Advanced Reactors, is to fulfill several objectives. The first objective is to complete and demonstrate/establish the process for predicting the microstructure and properties of structural materials in reactor and at high doses using ion irradiation as an accurate predictive tool for assessing behavior under reactor irradiation. The second objective is for this process to be adopted as part of an ASTM standard, and to work with the US Nuclear Regulatory Commission to utilize this standard in licensing decisions on advanced reactor designs for which such data are nonexistent or impractical to achieve. The need for standardized procedures is apparent based on the outcomes of a recent round-robin experiment from laboratories using ion beams for radiation damage studies and the roadmap for ion beam technologies to address challenges for the advancement of nuclear energy technologies.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Report on the Update and Codification of Expert Knowledge into ASTM Standard Practice E521
The overall purpose of the Grand Challenge Integrated Research Project (IRP), Accelerating the Qualification of Materials to Enable Rapid Deployment of Advanced Reactors, is to fulfill several objectives. The first objective is to complete and demonstrate/establish the process for predicting the microstructure and properties of structural materials in reactor and at high doses using ion irradiation as an accurate predictive tool for assessing behavior under reactor irradiation. The second objective is for this process to be adopted as part of an ASTM standard, and to work with the US Nuclear Regulatory Commission to utilize this standard in licensing decisions on advanced reactor designs for which such data are nonexistent or impractical to achieve. The need for standardized procedures is apparent based on the outcomes of a recent round-robin experiment from laboratories using ion beams for radiation damage studies and the roadmap for ion beam technologies to address challenges for the advancement of nuclear energy technologies.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
In situ irradiation of spent nuclear fuels
To improve the economics of commercial nuclear reactors, nuclear vendors and utilities are seeking approval for increased burnup usage of the existing nuclear fleet. This is especially critical for meeting the clean energy initiative by the United States Government, Department of Energy (DOE). However, one of the key challenges the nuclear industry faces in this regard is that the regions exposed to high burnup and low temperatures exhibit a fine-grained microstructure with large bubbles known as high-burnup structure (HBS) [1]. The formation of HBS has been correlated to the diminished performance of the reactor, as well as fuel fragmentation and pulverization during transient and accidental conditions [2]. Therefore, it is paramount to understand the mechanisms for HBS formation along with its impact on the properties and performance of nuclear fuels. While existing programs, such as Nuclear Energy Advanced Modeling and Simulation (NEAMS) and Advanced Fuel Campaign (AFC) are focusing on evaluating the performance impact of HBS, the physical mechanisms contributing to HBS formation are still not fully understood. In addition, having predictive capabilities and sound understanding of the microstructural evolution of nuclear fuel is essential for accelerated development, qualification, and deployment of new nuclear materials and novel reactor designs for advanced nuclear reactors. There is a lack of consensus among researchers regarding the mechanisms leading to such restructuring observed in HBS. Grain subdivision due to polygonization versus recrystallization, continuous versus discrete recrystallization occurring in tandem or conjunction, etc., have been proposed and debated. In general, it is hypothesized that defect accumulation and dislocation interaction within the grains cause the realignment of dislocations into grain boundaries, leading to the new subgrain formation, which over time transforms into new grains. However, due to the lack of transient data, the importance of fission rate, irradiation, thermal, and stress history of the fuel on the restructuring could not be assessed. In situ microstructural evolution under various irradiation conditions is desired to bridge this gap. Alternatively, phase-field-based models have been developed to capture HBS formation via discrete recrystallization utilizing the classical nucleation approach [3–5]. However, in these models, the grain nucleation criteria are often defined based on empirical relations for burnup and fission gas density leading to dislocation density change. A mechanistic approach to capture the dislocation interaction with the microstructural features leading to grain subdivision is lacking.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
High temperature polymer dielectric film-wire insulation
The highlights of the program are outlined including two major accomplishments. TRW identified and demonstrated the potential of two aromatic/heterocyclic polymers to have an outstanding and superior combination of electrical, thermal, and chemical resistance properties versus state-of-the-art Kapton for spacecraft and/or aircraft dielectric insulation applications. (Supporting data is provided in tables.) Feasibility was demonstrated for supporting/enabling technologies such as ceramic coatings, continuous film casting, and conductor wire wrapping, which are designed to accelerate qualification and deployment of the new wire insulation materials for USAF systems applications during the mid- to late-1990's.
Nairus, John G.↗
Large-Scale Calculations for Material Sciences Using Accelerators to Improve Time- and Energy-to-Solution
A major impediment to deploying next-generation high-performance computational systems is the required electrical power, often measured in units of megawatts. The solution to this problem is driving the introduction of novel machine architectures, such as those employing many-core processors and specialized accelerators. In this article, we describe the use of a hybrid accelerated architecture to achieve both reduced time to solution and the associated reduction in the electrical cost for a state-of-the-art materials science computation.
36 MATERIALS SCIENCE↗