Results for “Material” | Engineering Papers

DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗

DOE OSTI2026

The U.S. Fusion Materials Community Roadmap: Near-term research priorities for the development of plasma-facing and structural materials for fusion power plants

In response to the needs of a rapidly growing private fusion industry, the U.S. Fusion Materials Coordinating Committee (FMCC) and the broader U.S. fusion materials research community undertook an extensive effort to create a comprehensive roadmap for fusion materials development. The result of this effort was the U.S. Fusion Materials Community Roadmap (US-FMCR), which describes the steps needed to advance the technical maturity of leading candidates for plasma-facing materials and structural materials for fusion power plants from laboratory-scale experiments to a point of sufficient technological readiness for industrial adoption and implementation. However, researchers face significant resource constraints as well as very aggressive pilot plant development timelines. Thus, the research strategies detailed in the US-FMCR require further assessment to downselect the specific tasks that must be prioritized within the next two to three years, in order to make the most efficient use of funding, human resources, and experimental facilities. This paper presents an overview of the US-FMCR and its development process. We also present the subset of research objectives that the FMCC identified as the most urgent research priorities for the U.S. fusion materials research community. The state-of-the-art of materials research is also highlighted for each class of materials considered in the US-FMCR. The recommendations presented here integrate an extensive evaluation of the current status of fusion materials research with a broad cross-section of opinion from the wider U.S. fusion community.

Ferry, Sara [Massachusetts Institute of Technology↗

DOE OSTI2025

Materials laboratories of the future for alloys, amorphous, and composite materials

In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Trust Not Verify? The Critical Need for Data Curation Standards in Materials Informatics

The importance of data curation has been recognized in multiple areas of research; however, the discussion of this important issue is only beginning to emerge in materials science. In this Perspective, we highlight the benefits of using the standardized data curation protocols in materials science and discuss current gaps in accurate and reproducible data reporting using case studies drawn from high-impact materials science papers and well-known databases such as the Crystallography Open Database (COD) and the Cambridge Structural Database (CSD). We argue that both experimental and computational materials scientists need to embrace a culture of rigorous data curation as part of modern research data management. We propose a sample data curation pipeline for materials chemistry and illustrate its use by creating two new materials chemistry databases. Here, we hope that this perspective will serve to catalyze further discussion and promote the continuous development of rigorous data curation practices within the materials science research community. We posit that adherence to best practices of data curation will promote and enhance the reliability, reproducibility, and integrity of materials research and enable the development of reliable AI and machine learning models that critically depend on the use of quality data.

Chemical structure↗

DOE OSTI2025

Prioritizing Nuclear Materials for SAM-3 Neutron Irradiation Campaign: Structural and Cladding Materials Candidates

This report outlines a framework for selecting structural and cladding materials for the Nuclear Science User Facilities (NSUF) SAM-3 neutron irradiation campaign to support the advancement of nuclear energy technologies. The document begins with an introduction that provides background context, highlights the motivations for launching a new irradiation campaign, and defines the overall objectives. The core of the report describes the design considerations for the irradiation campaign, including capsule configurations, irradiation temperature ranges, and target dose levels (defined by displacements per atom, or dpa). The material recommendation was guided by the Specimen Identification and Prioritization (SIP) Working Group, a multidisciplinary team of experts representing national laboratories, academia, industry, federal government and agency. This group played a central role in identifying candidate materials, evaluating technical justifications, and ensuring alignment with boarder programmatic goals. A detailed set of criteria for material prioritization is then presented, taking into account reactor relevance, performance gaps, advanced manufacturing methods, and emerging material classes. Based on the input of SIP working group, specific materials were selected and justified for inclusion in the irradiation campaign by the NSUF leadership and its U.S. Department of Energy (DOE)-Office of Nuclear Energy (NE) management. The final section provides recommended capsule designs, summarizing critical parameters such as material type, fabrication method, sample geometry, irradiation conditions, and specimen quantities. This report serves as a foundation for executing a focused and high-impact neutron irradiation campaign aimed at addressing key materials challenges for both existing and advanced nuclear reactors.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Coupling Microstructural Evolution Simulations to Material Property Degradation Predictions for Plasma-Facing Materials

Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500 C to 1500 C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 D/m^2-s. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

The northeast materials database for magnetic materials

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R 2 ) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

Ferromagnetism↗

DOE OSTI2025

Primary material supply configurations and domestic recycling for cost-effective battery material production in the US

Battery cathode active material costs hinge on regionally concentrated, price-volatile metal supply. Here. we construct a regional facility-level cost model based on over 80 global lithium, cobalt, and nickel mines, refineries, and battery-grade material plants. Our model yields aggregated lithium, nickel, manganese, and cobalt production material costs from 392 region-based supply configurations for five different cathode active materials. Focusing on the United States, all-domestic supply is 9–34% costlier than global average, increasing by cobalt content, while these shortfalls can be overcome by selective low-cost material imports. Furthermore, we analyze costs of two U.S.-based recycling facilities from primary data and techno-economic modelling and compare resulting cathode active material-level costs to primary supply. Although it is still significantly higher on cathode active material cost-level, rising end-of-life flows and lowered black-mass prices will, however, make secondary supply cost-competitive to domestic and foreign primary supply cost floors. Facility-level benchmarks reveal targeted import, scaling, and production cost optimization as levers for a resilient, cost-effective U.S. battery-material supply chain.

Energy↗

DOE OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 1

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024). Three distinct rounds of FSP experiments were performed by the experimental team, producing replicate samples utilizing across different nominal processing conditions (Condition IDs) listed in Table 1. The starting material on which FSP was applied was commercially available unprocessed stainless-steel type 316L material. Chosen processing conditions were very diverse, and some were intentionally chosen to produce defects. Several samples experienced tool breakage during experimentation, so a full set of three replicates was not produced for every nominal processing condition.

36 MATERIALS SCIENCE↗

DOE OSTI2024

In-Silico Analysis of High Refractive Index Materials Through Principles of Materials Design

The intent of the paper is to use specific principles of Materials Design that were developed and applied in the electronics industry for enabling understanding and design of improved high refractive index materials. Further, by combining first-principle based ab-initio, semiempirical interatomic potential methods, and machine learning approaches in conjunction with experimental data, we identified specific determinants of high refractive index materials, which can be critically applied for informing materials design and accelerating discovery. Specifically, it was demonstrated that chalcogenides and perovskites as bulk materials can exhibit higher refractive indices with appropriate engineering of specific aspects of the materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Accelerated Exploration of Empty Material Compositional Space: Mg–Fe–B Ternary Metal Borides

Explore the source record for details and available documents.

Zhang, Zhen↗

DOE OSTI2025

Computational materials assessment of the D/Li-stripping neutron source as a prototypical facility for fusion materials testing

As the US fusion materials community awaits the selection and design of a fusion prototypical neutron source (FPNS), a risk reduction exercise has been conducted to (i) provide an updated materials performance evaluation using state-of-the-art computational materials modeling, (ii) expand on legacy analysis based on pure Fe to other relevant fusion structural materials types, and (iii) ensure that materials response under FPNS operational conditions is consistent with referential fusion reactor conditions. The current paper describes the efforts undertaken to assemble a comprehensive computational methodology that includes neutronics, primary damage calculations, atomistic simulations of displacement cascades, chemical inventory evolution calculations, and a computational thermodynamic analysis of emerging phases during irradiation. Our work extends existing studies in pure Fe to reduced-activation ferritic/martensitic steels, tungsten, silicon carbide, and vanadium alloys. We focus on the single-beam deuteron/lithium-stripping neutron source behind the IFMIF-DONES concept, which we assess against ITER, two DEMO designs, and an ideal pure 14-MeV flux. Our analysis indicates that, within standard uncertainties inherent to the models employed, the DONES concept adequately captures fusion conditions in the four materials analyzed. Our work is intended as a comprehensive irradiation damage analysis of fusion-representative neutron sources, to be used for further neutron source evaluation and fusion facility operation.

Marian, Jaime [Univ. of California, Los Angeles, C↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 2

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 3

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 4

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2025

Materials Data Science Ontology(MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science

Ontologies have gained popularity in the scientific community as a way to standardize terminologies in organizations’ data. Although certain cohorts have created frameworks with rules and guidelines on creating ontologies, there exist significant variations in how Materials Science ontologies are currently developed. We seek to provide guidance in the form of a unified automated framework for developing interoperable and modular ontologies for Materials Data Science that simplifies the ontology terms matching by establishing a semantic bridge up to the Basic Formal Ontology(BFO). This framework provides key recommendations on how ontologies should be positioned within the semantic web, what knowledge representation language is recommended, and where ontologies should be published online to boost their findability and interoperability. Two fundamental components of the MDS-Onto framework are the bilingual package called FAIRmaterials for ontology creation and FAIRLinked, for FAIR data creation. To showcase the practical capabilities of FAIRmaterials, we present two exemplar domain ontologies of MDS-Onto: Synchrotron X-Ray Diffraction and Photovoltaics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Development Results on Replacement Materials for Current Scarce or High Supply Chain Risk Materials

In September 2020, the U.S. government issued an executive order to address the threat to the domestic supply chain from its reliance on critical minerals (CMs) from foreign competitors and to support the domestic mining and processing industry. The Advanced Materials and Manufacturing Technology (AMMT) program is addressing this executive order by evaluating advanced manufacturing (AM) and its impact on the demands of CMs for energy production in general and how the deployment of AM in nuclear energy will support the projected goals of the Paris Accord and further a net-zero carbon economy (NZE) by 2050. Three strategic reports were previously prepared by the AMMT program to date and identified two areas for more detailed exploration: (1) the replacement of high-risk CMs such as cobalt and niobium by more abundant minerals and (2) the minimization and utilization of CM waste streams. The design of nuclear materials without critical elements as alloying elements, is a part of the nuclear materials strategy to overcome the critical minerals scarcity. In this report, two approaches are evaluated namely (1) replacement of critical elements as alloying elements in nuclear materials, and (2) the design of a new alloys that does not contain critical minerals as an alloying element.

36 MATERIALS SCIENCE↗