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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

C-HER Metadata Overview: Approach, Standards, and Rigor for the Centralized Health and Exposomic Resource

The Centralized Health and Exposomic Resource (C-HER) unifies environmental, demographic, geographic, and health-related data for exposomic research. The source data differ in format, geographic coverage, time period, resolution, terminology, and documentation. We use a common metadata framework to describe those differences and to record how each data resource has been processed, documented, and ingested. This document relates only to the C-HER metadata framework. It explains the information that is recorded for each resource, the standards used to organize that information, the conditions for metadata completeness, and the relationship between metadata and quality review. It is intended for those who need to understand what C-HER metadata communicates and how it supports appropriate use of the data. It is not an implementation specification or procedure. It does not document the database schema, source code, deployment configuration, transformation algorithms, or dataset-specific QA/QC thresholds. Those materials are maintained separately.

MacFarland, Midgie [ORNL] (ORCID:0009000807354078)↗
NASA NTRS1995

Materials engineering data base

The various types of materials related data that exist at the NASA Marshall Space Flight Center and compiled into databases which could be accessed by all the NASA centers and by other contractors, are presented.

Source record↗
DOE OSTI2025

Metallic fuel transient fuel-cladding interface liquefaction model assessment platform enabled by integrating BISON with databases

A novel platform has been developed within the BISON fuel performance code to assess models of fuel-cladding interface liquefaction for sodium-cooled fast reactor (SFR) metallic fuels. Here, this platform is crucial because liquefaction at the fuel-cladding interface significantly impacts fuel performance and may compromise fuel pin integrity during transient events. To ensure accurate predictions, the platform integrates data collected during the Integral Fast Reactor (IFR) program, now archived in metallic fuel databases. This integration supports verification and validation (V&V) of the models in BISON. Leveraging the extensive US experience with metallic fuel liquefaction and the collections of preserved legacy data, the platform serves as a powerful tool for evaluating existing models and advancing the development of new ones.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Assessment of the frequency and nature of erroneous x-ray photoelectron spectroscopy analyses in the scientific literature

Here, this study was undertaken to understand the extent and nature of problems in x-ray photoelectron spectroscopy (XPS) data reported in the literature. It first presents an assessment of the XPS data in three high-quality journals over a six-month period. This analysis of 409 publications showing XPS spectra provides insight into how XPS is being used, identifies the common mistakes or errors in XPS analysis, and reveals which elements are most commonly analyzed. More than 65% of the 409 papers showed fitting of XP spectra. An ad hoc group (herein identified as “the committee”) of experienced XPS analysts reviewed these spectra and found that peak fitting was a common source of significant errors. The papers were ranked based on the perceived seriousness of the errors, which ranged from minor to major. Major errors, which, in the opinion of the ad hoc committee, can render the interpretation of the data meaningless, occurred when fitting protocols ignored underlying physics and chemistry or contained major errors in the analysis. Consistent with other materials analysis data, ca. 30% of the XPS data or analysis was identified as having major errors. Out of the publications with fitted spectra, ca. 40% had major errors. The most common elements analyzed by XPS in the papers sampled and researched at an online database, include carbon, oxygen, nitrogen, sulfur, and titanium. A scrutiny of the papers showing carbon and oxygen XPS spectra revealed the classes of materials being studied and the extent of problems in these analyses. As might be expected, C 1s and O 1s analyses are most often performed on sp2-type materials and inorganic oxides, respectively. These findings have helped focus a series of XPS guides and tutorials that deal with common analysis issues. The extent of problematic data is larger than the authors had expected. Quantification of the problem, examination of some of the common problem areas, and the development of targeted guides and tutorials may provide both the motivation and resources that enable the community to improve the overall quality and reliability of XPS analysis reported in the literature.

Major, George H.↗
DOE OSTI2021

Full Moment Tensor Solutions of U.S. Underground Nuclear Tests for Event Screening and Yield Estimation

Moment tensor (MT) solutions are proving increasingly valuable in explosion monitoring, especially now that they are more routinely calculated for the unconstrained, full (six component) MT. In this study, we have calculated MTs for U.S. underground nuclear tests conducted at the Nevada National Security Site using seismic recordings primarily from the Livermore Nevada Network. We are able to determine them for 130 nuclear explosions from 1970 to 1996 for a range of yields and under a variety of material conditions, which we have supplemented with 10 additional chemical explosions at the test site. The result is an extensive database of MTs that can be used to assess the performance of important monitoring tasks such as event identification and yield determination. We test the explosion event screening on the fundamental lune of the MT eigensphere and find MT screening to be a robust discriminant between earthquakes and explosions. We then explore the estimation of moment-derived yield, in which we find that material properties are the largest contributor to differences in the estimated moment-to-yield ratio. Additionally, further research conducted on this dataset can be used to develop, test, and improve various explosion monitoring methodologies.

58 GEOSCIENCES↗
DOE OSTI2025

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗
NASA NTRS2000

From Interstellar PAHs and Ices to the Origin of Life

Tremendous strides have been made in our understanding of interstellar material over the past twenty years thanks to significant, parallel developments in observational astronomy and laboratory astrophysics. Twenty years ago the composition of interstellar dust was largely guessed at, the concept of ices in dense molecular clouds ignored, and the notion of large, abundant, gas phase, carbon rich molecules widespread throughout the interstellar medium (ISM) considered impossible. Today the composition of dust in the diffuse ISM is reasonably well constrained to micron-sized cold refractory materials comprised of amorphous and crystalline silicates mixed with an amorphous carbonaceous material containing aromatic structural units and short, branched aliphatic chains. In dense molecular clouds, the birthplace of stars and planets, these cold dust particles are coated with mixed molecular ices whose composition is very well constrained. Lastly, the signature of carbon-rich polycyclic aromatic hydrocarbons (PAHs), shockingly large molecules by earlier interstellar chemistry standards, is widespread throughout the Universe. The first part of this lecture will describe how infrared studies of interstellar space, combined with laboratory simulations, have revealed the composition of interstellar ices (the building blocks of comets) and the high abundance and nature of interstellar PAHs. The laboratory database has now enabled us to gain insight into the identities, concentrations, and physical state of many interstellar materials. Within a dense molecular cloud, and especially in the solar nebula during the star and planet formation stage, the materials frozen into interstellar/precometary ices are photoprocessed by ultraviolet light, producing more complex molecules. The remainder of the presentation will focus on the photochemical evolution of these materials and the possible role of these compounds on the early Earth. As these materials are thought to be the building blocks of comets and related to the carbonaceous components of micrometeorites, they are likely to have been important sources of complex organic materials on the early Earth and their composition may be related to the origin of life.

Allamandola, Louis J.↗
NASA NTRS2004

From Interstellar Polycyclic Aromatic Hydrocarbons and Ice to the Origin of Life

Tremendous strides have been made in our understanding of interstellar material over the past twenty years thanks to significant, parallel developments in observational astronomy and laboratory astrophysics. Twenty years ago the composition of interstellar dust was largely guessed at, the concept of ices in dense molecular clouds ignored, and the notion of large, abundant, gas phase, carbon rich molecules widespread throughout the interstellar medium (ISM) considered impossible. Today the composition of dust in the diffuse ISM is reasonably well constrained to cold refractory materials comprised of amorphous and crystalline silicates mixed with an amorphous carbonaceous material containing aromatic structural units and short, branched aliphatic chains. In the dense ISM, the birthplace of stars and planets, these cold dust particles are coated with mixed molecular ices whose composition is very well constrained. Lastly, the signature of carbon-rich polycyclic aromatic hydrocarbons (PAHs), shockingly large molecules by early interstellar chemistry standards, is widespread throughout the Universe. The first part of this talk will describe how infrared studies of interstellar space, combined with laboratory simulations, have revealed the composition of interstellar ices (the building blocks of comets) and the high abundance and nature of interstellar PAHs. The laboratory database has now enabled us to gain insight into the identities, abundances, and physical state of many interstellar materials. Within a dense molecular cloud, and especially in the presolar nebula, the materials frozen into the interstellar/precometary ices are photoprocessed by ultraviolet light and produce more complex molecules. The remainder of the presentation will focus on the photochemical evolution of these materials and the possible role of these compounds on the to the carbonaceous components of micrometeorites, they are likely to have been important sources of complex materials on the early Earth and their composition may be related to the origin of life.

Allamandola, Louis↗
DOE OSTI2025

Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materials or manufacturing techniques. Large language models (LLMs) could potentially bridge the gap. Their capacity to store and retrieve data from large databases provides them with a breadth of knowledge across disciplines. However, their generalized knowledge base can lack targeted, industry-specific knowledge. To this end, we present two LLM-based applications based on the GPT-4 architecture: (1) The Composites Guide: a system that provides expert knowledge on composites material and connects users with research and industry professionals who can provide additional support and (2) The Equipment Assistant: a system that provides guidance for manufacturing tool operation and material characterization. By combining the knowledge of general AI models with industry-specific knowledge, both applications are intended to provide more meaningful information for engineers. In this paper, we discuss the development of the applications and evaluate it through a benchmark and two informal user studies. The benchmark analysis uses the Rouge and Bertscore metrics to evaluate our models’ performance against GPT-4o. The results show that GPT-4o and the proposed models perform similarly or better on the ROUGE and BERTScore metrics. The two user studies supplement this quantitative evaluation by asking experts to provide qualitative and open-ended feedback about our model’s performance on a set of domain-specific questions. The results of both studies highlight a potential for more detailed and specific responses with the Composites Guide and the Equipment Assistant.

Kapoor, Gunnika [Oak Ridge National Laboratory (OR↗
DOE OSTI2021

Construction of an Anion-Pillared MOF Database and the Screening of MOFs Suitable for Xe/Kr Separation

The separation of xenon/krypton (Xe/Kr) mixtures is a challenging process. Many porous materials allow the adsorption of both Xe and Kr but only with low selectivity. Anion-pillared metal–organic frameworks (MOFs), featuring the anion groups as structural pillars, show potential in gas separations, but only a limited number of them have been synthesized. Here, we describe a collection of 936 anion-pillared MOFs based on 22 experimentally available structures. We performed density functional theory (DFT) optimization and then assigned density-derived electrostatic and chemical (DDEC) charges for each MOF to make them well suited to molecular simulations. The structural properties of the MOFs vary more strongly with the choice of the organic ligand than with other aspects like fluorine groups and metal centers. We then screened the entire collection of MOFs in the context of Xe/Kr separation at room temperature. Compared with previously reported MOFs, the interpenetrated MOF SIFSIX-6-Cd-i is predicted to perform better for Xe/Kr separations, with a good balance between working capacity (1.62 mmol/g) and separation selectivity (16.4) at 298 K and 100 kPa. Here, we also found that the heterogeneity of fluorine groups within a MOF can help to enhance Xe working capacity without reducing the Xe/Kr selectivity, suggesting that synthesis of anion-pillared MOFs with mixed fluorine groups may lead to improved Xe/Kr separation performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗
NASA NTRS1995

From LDEF to a national Space Environment and Effects (SEE) program: A natural progression

As the LDEF program draws to a close, it leaves in place the fundamental building blocks for a Space Environment and Effects (SEE) program. Results from LDEF data analyses and investigations now form a substantial core of knowledge on the long term effects of the space environment on materials, system and structures. In addition, these investigations form the basic structure of a critically-needed SEE archive and database system. An agency-wide effort is required to capture all elements of a SEE program to provide a more comprehensive and focused approach to understanding the space environment, determining the best techniques for both flight and ground-based experimentation, updating the models which predict both the environments and those effects on subsystems and spacecraft, and, finally, ensuring that this multitudinous information is properly maintained, and inserted into spacecraft design programs. Many parts and pieces of a SEE program already exist at various locations to fulfill specific needs. The primary purpose of this program, under the direction of the Office of Advanced Concepts and Technology (OACT) in NASA Headquarters, is to take advantage of these parts; apply synergisms where possible; identify and when possible fill-in gaps; coordinate and advocate a comprehensive SEE program. The SEE program must coordinate and support the efforts of well-established technical communities wherein the bulk of the work will continue to be done. The SEE program will consist of a NASA-led SEE Steering Committee, consisting of government and industry users, with the responsibility for coordination between technology developers and NASA customers; and Technical Working Groups with primary responsibility for program technical content in response to user needs. The Technical Working Groups are as follows: Materials and Processes; Plasma and Fields; Ionizing Radiation; Meteoroids and Orbital Debris; Neutral External Contamination; Thermosphere, Thermal, and Solar Conditions; Electromagnetic Effects; Integrated Assessments and Databases. Specific technology development tasks will be solicited through a NASA Research Announcement to be released in May of 1994. The areas in which tasks are solicited include: (1) engineering environment definitions, (2) environments and effects design guidelines, (3) environments and effects assessment models and databases, and (4) flight/ground simulation/technology assessment data.

Bowles, David E.↗
NASA NTRS2001

Characterization of Ceramic Matrix Composite Combustor Components: Pre and Post Exposure

The pursuit of lower emissions and higher performance from gas turbine engines requires the development of innovative concepts and the use of advanced materials for key engine components. One key engine component is the combustor, where innovative design and material improvements have the potential to lower emissions. Efforts to develop a High Speed Civil Transport with low emissions were focused on the evaluation of combustor concepts with liners fabricated from a ceramic matrix composite of silicon carbide fibers in a silicon carbide matrix (SiC/SiC). The evaluation of SiC/SiC composites progressed from simple coupons (to establish a first-order database and identify operant failure mechanisms and damage accumulation processes), to feature-based subelements (to assess fabricability and in situ material response), to actual components (to assess structural integrity, dimensional, and compositional fidelity) tested under simulated engine conditions. As in the case of all evolutionary material and process work, a key element to resolving fabrication issues is the evaluation of witness areas taken from fabricated components before testing the actual component. The witness material from these components allowed microstructural and mechanical testing to be performed and compared to the ideal, flat panel, conditions and data that are typical of basic characterization. This also allowed samples of similar design to be taken from components after 115 hours of combustion exposure. Testing consisted of tensile, double notch shear, ring burst, and thermal conductivity that sampled various regions of the components. The evaluation of the witness material allowed an understanding of the fabrication process, highlighting critical issues, in an early phase of the learning curve development of these configuration and material unique parts. Residual property testing, after exposure, showed if degradation of the material under actual service conditions was occurring. This paper will present the results of this critical testing. Evaluations that consider the degree of complexity of the fabricated part were established to relate in situ performance to that of flat panel based coupons. Nondestructive evaluation was used throughout the evaluation process. These results will also be discussed as an aid in understanding the test results.

Ojard, G.↗
DOE OSTI2021

Machine learning for polymeric materials: an introduction

We report that Polymers are incredibly versatile materials and have become ubiquitous. Increasingly, researchers are using data science and polymer informatics to design new materials and understand their structure-property relationships. Polymer informatics is an emerging field. While there are many useful tools and databases available, many are not widely utilized. Herein, we introduce the field of polymer informatics and discuss some of the available databases and tools. We cover how to share polymer data, approaches for preparing a dataset for machine learning and recent applications of machine learning to polymer property prediction and polymer synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Industry Facing PV Degradation Prediction Tool and Database to Enable a 50-Year Life Module

The Photovoltaic (PV) industry constantly aims for lower costs, higher-efficiency cells, and improved module designs. These trends lead to using new materials, designs, and manufacturing processes, resulting in a continually changing technological landscape. These changes can potentially introduce new, unknown degradation mechanisms and failure modes that are difficult to diagnose, analyze, test, and model. This introduces uncertainty into the expected lifetime of PV modules of 25 years. Furthermore, research efforts aim for up to 50 years of service life while keeping performance degradation at a minimum for decades of outdoor weathering - putting additional pressure on improving the accuracy of long-term durability and reliability assessments. Here there is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. Because the core of this type of analysis involves calculations that are complicated but ubiquitous for many degradation processes, an enhanced predictive modeling framework will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this work, we present an online tool that can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. A graphical user interface will aid in the understanding of the results. The prediction tool will be built modular and published as open source, enabling users to expand on the existing framework. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database to perform geospatial degradation analysis in the US and worldwide. Our repository will contain various degradation models and material parameters suitable for the reliability and durability assessment of materials and components deployed outdoors. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry.

degradation↗
NASA NTRS2019

NASA’s In-Space Manufacturing Project: Update on Manufacturing Technologies and Materials to Enable More Sustainable and Safer Exploration

NASA’s In-Space Manufacturing (ISM) project seeks to develop the materials, processes, and manufacturing technologies needed to provide an on-demand manufacturing capability for deep space exploration missions. The ability to manufacture and recycle some parts on demand rather than launch them from earth has the potential to reduce logistics requirements on long duration missions and enhance crew safety. With the launch of the first 3D printer (built and operated by Made in Space through a Small Business Innovative Research – SBIR -- contract) to the International Space Station (ISS) in 2014, the ISM project demonstrated the feasibility of operating an on-demand manufacturing system in a microgravity environment. This paper will provide an update on recent advancements in ISM under three key technology areas: manufacturing, recycling, and development of a design database. ISM continues to pursue development of manufacturing technologies for space applications and use the ISS as a critical test bed to prove out these technologies before deploying them on next generation exploration systems. Activities under this focus area include: characterization of materials manufactured using the Additive Manufacturing Facility (AMF), the second generation commercial 3D-printer on ISS, also owned and operated by Made in Space; development of prototype payloads for metal manufacturing through phase II SBIR contracts with Tethers Unlimited, Made in Space, and Ultra Tech Machinery; development of a multi-material fabrication laboratory capable of processing metals and providing inspection of manufactured parts through a Broad Agency Announcement (Techshot, Interlog, and Tethers Unlimited); an in-line sensing system for ISM platforms; and development of higher strength feedstocks for 3D polymer printers. In the area of recycling, the Tethers Unlimited Refabricator payload (an integrated 3D printer and recycler for ULTEM 9085) launched to ISS in November 2018 and began operating in early 2019. This payload represents the first demonstration of on-orbit recycling; down massed specimens will assess material degradation in the polymer over multiple recycling cycles to define limits on material re-use. Other work in the recycling area includes development of common use materials intended to be reused and recycled on space missions (Tethers Unlimited and Cornerstone Research Group) and a sterilization capability for multiple-use materials (ERASMUS from Tethers Unlimited). Concurrent with manufacturing technology and materials development work is creation of a design database, a curated list of parts that can be manufactured using the suite of In-Space Manufacturing capabilities.

Tracie Prater↗
NASA NTRS2020

Cerium Stable Isotope Analysis of Synthetic and Terrestrial Rock Reference Materials by MC-ICPMS

Cerium is the most abundant rare earth element (REE) in the solar photosphere, CI chondrites, and the Earth. It has four main stable isotopes (masses: 136,138,140, and 142), with 138Ce being the most studied species, used in geochronology and petrogenesis. In addition, more abundant 140Ce and 142Ce are suggested to be potentially applicable in geochemical investigations. In this work, we developed a modified four-step ion chromatography procedure for Ce chemical separation. Using a MC-ICPMS, we designed a cup configuration to measure 142Ce/140Ce ratio of the samples with an optimized Nd correction equation. A 0.03‰ (2SD) reproducibility was obtained for Ce Ames metal standard. We analyzed ten different igneous and one sedimentary geochemical reference materials. Mean δ142Ce range from −0.07 to 0.32‰. Most of the samples show a heavier Ce isotopic composition than the Ce Ames standard. The majority of rocks have a homogenous δ142Ce. The δ142Ce does not show any correlation with rock chemical composition including their Ce content or rock types. A carbonatite (SARM 40) has a mean δ142Ce of −0.07 ± 0.13‰ (2SD), lower than the other rocks, suggesting the possibility of a pronounced isotopic fractionation. Our work demonstrates the applicability of the developed methodology and the potential of Ce stable isotopes for future geochemical studies. Production of a larger database of δ142Ce values is required to obtain a clearer view on the similarities and differences between different geological material and explaining Ce stable isotope dynamics.

Cerium↗
DOE OSTI2025

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

36 MATERIALS SCIENCE↗