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DOE OSTI2022

Modeling antiphase boundary energies of Ni 3 Al-based alloys using automated density functional theory and machine learning

Antiphase boundaries (APBs) are planar defects that play a critical role in strengthening Ni-based superalloys, and their sensitivity to alloy composition offers a flexible tuning parameter for alloy design. Here, we report a computational workflow to enable the development of sufficient data to train machine-learning (ML) models to automate the study of the effect of composition on the (111) APB energy in Ni 3 Al-based alloys. We employ ML to leverage this wealth of data and identify several physical properties that are used to build predictive models for the APB energy that achieve a cross-validation error of 0.033 J m –2 . We demonstrate the transferability of these models by predicting APB energies in commercial superalloys. Moreover, our use of physically motivated features such as the ordering energy and stoichiometry-based features opens the way to using existing materials properties databases to guide superalloy design strategies to maximize the APB energy.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Test Results of the CERN HL-LHC low- Quadrupole Short Models MQXFS3c and MQXFS4

For the High Luminosity Upgrade of the CERN Large Hadron Collider, lower * quadrupole magnets based on advanced Nb3Sn conductors will be installed on each side of the ATLAS and CMS experiment insertion zones. As part of the technological developments needed to achieve the required field gradient of 132.6 T/m within a 150-mm aperture, short length model magnets, named MQXFS, are tested both at the CERN SM18 and FERMI Lab test facilities. The model magnets rely on two types of Nb3Sn conductors (RRP and PIT) and on an innovative bladders and keys design to provide mechanical support against the Lorentz forces. In 2016 and 2017, the powering tests of the first two models MQXFS3 (RRP) and MQXFS5 (PIT) proved that nominal performance (16.5 kA) could be reached with excellent memory of the quench current after thermal cycle. However both magnets showed a slow training behavior with clear observations of voltage disturbances before the quench. Besides MQXFS5 only could reach ultimate current (17.9 kA) whereas erratic behavior was observed on MQXFS3 due to conductor local degradation at the head of one of the coil. In 2018, this limiting coil was changed and the applied azimuthal pre-stress increased. If ultimate current could then be reached, no stable current could be maintained due to identified defect on the outer layer of the new coil. Finally the outcome of the test of the new model MQXFS4, featuring the final RRP conductors that will be used for the series production and variation on the inner layer quench heater designs are here reported in details.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Multi-scale modeling of the electric field assisted sintering process

The electric field assisted sintering (EFAS) process involves tightly coupled physics that influence microstructural evolution in the particles being compacted. It is also an inherently multi-scale phenomenon, with the microstructure of the compact influencing the subsequent engineering-scale response of the sintering system. To improve understanding of how processing parameters influence microstructural evolution, we have developed a multi-scale modeling approach that couples a continuum-level model of the sintering system with a phase-field model for microstructural evolution of particles within the compact. The phase-field model couples the effect of chemical and electrical driving forces on microstructural evolution and includes the effect of charged defect segregation to surfaces and grain boundaries; this segregation leads to enhanced defect transport and heat generation at these interfaces in response to applied electric field. The effect of enhanced heat generation on particle neck growth and the influence of microstructural evolution on the engineering-scale model are demonstrated.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multi-scale modeling of the electric field assisted sintering process

36 - MATERIALS SCIENCE↗

DOE OSTI2022

Structures and localized vibrational states of defects in graphite by tight-binding calculations

The structural and vibrational properties of pristine graphite and point defects in graphite are studied by tight-binding (TB) calculations using a three-center TB potential model. We showed that the three-center TB potential without ‘‘ad hoc’’ van der Waals interaction corrections can accurately describe the inter-layer distance of graphite and the lowest-energy structures and stabilities of typical point defects in graphite. The results from our TB calculations are in good agreement with those from density-functional theory calculations with van der Waals interaction corrections. We also investigated the vibrational properties to gain better understanding on the localization of vibrational states induced by the point defects. Our calculation results show that although localized or quasi-localized vibrational modes can be found in all defected graphite, the localization induced by Frenkel pair, dual-vacancy, and dual-interstitial defects is much stronger. Atomic displacements associated with the localized vibrational modes induced by these three point defects are also analyzed

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

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↗

DOE OSTI2025

Aerodynamic Characterization of 3D Scanned Wind Turbine Blades Using Experimental and Computational Methods

This study presents an aerodynamic characterization of 3D scanned wind turbine blades using both experimental and computational methods. The research was conducted by Gulf Wind Technology and Sandia National Laboratories. The primary objective was to investigate the aerodynamic impacts of leading-edge manufacturing defects on wind turbine blades. The study utilized the Stratasys NEO 800 3D Printer for high-precision manufacturing and the GWT Accelerator Wind Tunnel for experimental testing. Computational simulations were performed using COMSOL Multiphysics to model the wind tunnel and analyze flow characteristics and OpenFOAM to study the aerodynamic impacts of leading-edge defects. OpenFAST was used to estimate how these defects can lead to revenue losses for wind farm operators as high as 6%. The results demonstrated significant aerodynamic performance variations due to defects, with detailed analysis provided through wind tunnel and CFD data. The findings contribute to the understanding of defect impacts on wind turbine blade performance and offer insights for future design improvements.

17 WIND ENERGY↗

DOE OSTI2024

DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.

anomaly detection↗

DOE OSTI2019

Phonon-induced multicolor correlations in hBN single-photon emitters

Color centers in hexagonal boron nitride have shown enormous promise as single-photon sources, but a clear understanding of the electron-phonon interaction dynamics is critical to developing the material for quantum communications or quantum simulations. We demonstrate photon antibunching in the filtered auto- and cross-correlations g (2) lm (τ) between zero-, one-, and two-phonon replicas of defect luminescence. With no background correction, we observe a single-photon purity of g (2) (0) = 0.20 in a phonon replica and cross-spectral correlations of g (2) lm (0) = 0.18 between a phonon replica and the zero-phonon line. Furthermore, we combine a violation of the Cauchy-Schwarz inequality and a phenomenological model of the phonon-mediated emission cross section to distinguish a low quantum-efficiency defect from phonon replicas of a bright defect.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Coherent Spins in van der Waals Semiconductor GeS 2 at Ambient Conditions

Optically active spin defects in van der Waals (vdW) materials have emerged as versatile quantum sensors, enabling applications for a wide range of quantum phenomena in condensed matter systems. Their ease of exfoliation and compatibility with device integration make them promising candidates for future quantum technologies. Here we report the observation and room-temperature coherent control of ensemble spin defects in the high-temperature crystalline phase of germanium disulfide (β-GeS 2 ), a two-dimensional (2D) semiconductor with low nuclear spin density. The defects exhibit spin-1/2 behavior, and their dynamics can be explained by a weakly coupled spin-pair model. We implement dynamical decoupling techniques to extend the coherence time (T 2 ) by a factor of 20. Finally, we use density functional theory (DFT) calculations to estimate the structures and spin densities of two possible spin defect candidates. This work will help to expand the field of quantum sensing with spin defects in 2D materials.

2D materials↗

DOE OSTI2025

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes.

Reimer, C↗

DOE OSTI2024

Understanding Formation of Irradiation-Induced Defects through 4D-STEM, Electron Tomography, and WBDF-STEM

A major challenge in advancing nuclear materials for next-generation fission and proposed fusion reactors is to comprehensively understand the formation of irradiation-induced defects. Here it is essential to correlate the evolution of irradiation-induced defects and the degradation of mechanical properties, as they collectively dictate the material's lifespan and ensure nuclear safety. Scanning transmission electron microscopy (STEM) based techniques have emerged as indispensable tools for irradiation-induced defect characterization, offering high spatial resolution imaging and chemical analysis, such as electron energy loss spectroscopy (EELS) and energy dispersive X-ray spectroscopy (EDXS). These techniques have been effectively used to obtain an atomic-scale view of the defect structure. Recent advances in electron microscopy, particularly in 4D-STEM, offer detailed insight into microstructural evolution by capturing full 2D diffraction patterns at every pixel position. Using high-speed direct electron detectors, this technology generates a four-dimensional dataset, overcoming the limitations of traditional STEM imaging.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Bayesian modeling of inconsistent plastic response due to material variability

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗

DOE OSTI2024

Surface Dynamics of Selective Hydration of Rutile TiO 2 : A Kinetic Monte Carlo Approach

The utility of atomic layer deposition (ALD) continues to expand beyond conformal thin-film fabrication to include area- or site-selective ALD. We previously identified a strategy for site-selective ALD (SS-ALD) via the evaluation of thermodynamically preferred hydration of rutile TiO 2 surfaces, as deduced from electronic structure calculations. Here, we report a novel kinetic Monte Carlo (KMC) model that allows for the investigation of surface dynamics and kinetics that improves our understanding of and intuition for the selective hydration strategy. Here we demonstrate the validity of the strategy with respect to step-edge defects for the lowest energy (110) facet as well as report results for the other common facets which agree with experimental STM observations. The results here indicate that the selective hydration strategy is feasible both thermodynamically (evaluated in our previous publication) and kinetically (from the KMC model). Because diffusion has a slower rate than others, we find that any proximity effects between terrace and defect sites are unlikely to affect the selective hydration strategy for rutile TiO 2 . The KMC model further provides relevant timescales for achieving selectivity experimentally and establishes the kinetic viability of the selective hydration approach to SS-ALD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Interactions between displacement cascade and dislocation and their influences on Peierls stress in Fe-20Cr-25Ni alloys

The interactions between dislocations and displacement cascades are investigated using molecular dynamics simulations. The primary knock-on atoms (PKAs) are placed at different distances to edge and screw dislocation lines. Specifically, an fcc Fe-20Cr-25Ni system is studied; the alloy is the based alloy for Alloy 709. The number of defects is calculated using Wigner-Seitz analysis in order to illustrate the effect of dislocations on cascades. The displacement cascades in systems containing dislocations tend to generate more total defects compared with bulk systems, but eventually lead to less surviving defects in the materials matrix due to interactions with dislocations. The changes in atomic structures of the dislocations after interacting with cascades are analyzed to understand how the primary damage affects dislocations. The displacement cascade potentially causes dislocation climb in edge dislocations and cross-slip in screw dislocations, which could serve as additional mechanisms contributing to the radiation-induced hardening (RIH) compared with conventional RIH models. To reveal this, Peierls stresses are calculated before and after cascades using molecular static simulations. This study provides critical information of how defect production and dislocations are correlated, especially when total dose is high, which needs to be taken into account in upper scale models, such as mean-field rate theory.

Lu, Zizhe↗

DOE OSTI2025

Review of radiation-induced defects in GaAs

Radiation-resilient optoelectronic materials are highly desired in space and nuclear applications, and understanding the relevant defects and electronic processes in those materials is crucial for both current and next-generation applications. GaAs is a prototypical semiconductor that serves as a foundational system for describing and understanding optoelectronic devices. In this review, both experimental studies and molecular dynamics (MD) simulations of irradiated GaAs are reviewed, with particular emphasis on the deep-level transient spectroscopy, irradiation-induced amorphization zones, and MD predictions of damage structures. The MD results are also compared to predictions of the non-ionized energy-loss model. Recent theoretical studies, in particular, density functional theory based calculations on the simple intrinsic defects and defect clusters in GaAs, are also reviewed. These defects have an important role in dictating the evolution of GaAs in radiation damage environments, as they impact the coupled dynamics of charge carriers. Finally, possible gaps and challenges toward the general understanding of defect evolution in GaAs are discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Experimental and computational analysis of failure mechanisms in unidirectional carbon fiber reinforced polymer laminates under longitudinal compression loading

Here, his study investigates the failure mechanisms of notched unidirectional (UD) carbon fiber reinforced polymer (CFRP) laminates subjected to longitudinal compressive loading. A sequence of failure initiation and propagation is observed based on optical microscopy images of specimens during failure. The micrographs reveal that the main failure mechanism in UD laminates is fiber kinking failure. The influence of manufacturing defects and voids on fiber kinking mechanisms is also analyzed. The results show that the voids in the resin-rich area lead to kink-band splitting, while in some cases the voids cause the kink-band to deflect in a new direction depending on the location of the voids. The next focus of this study is to propose a computational micromechanics model considering local fiber waviness to study the fiber kinking failure mechanism. The computational results on kink-band formation and stress-strain response show good consistency with our experimental analysis. Lastly, the failure envelopes of σ 11 -σ 22 and σ 11 -τ 12 subjected to combined loading conditions are obtained from the computational model, which provide essential inputs for future theoretical failure criteria.

36 MATERIALS SCIENCE↗