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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Thermodynamics of Tritium Trapping by Point Defects at Interfacial Cr-based Phases of the Al Coating

Density functional theory (DFT) simulations have been carried out to evaluate the potential for tritium trapping by metal vacancies in four phases Cr-containing phases (i.e., Cr3Si, Al0.3Cr0.7, Al8Cr5-HT, and Al8Cr5) identified near the interface between the Al coating and 316 stainless steel (316 SS) cladding. In addition, an ab initio thermodynamics approach has been employed to predict the temperature and T2-partial pressure dependence on the thermodynamics of singly tritiated defects. Key results in this work suggest that metal vacancies in the four phases have the potential to favorably trap tritium species, especially Si vacancies in Cr3Si phase. This overall thermodynamic trend can be correlated to the energy cost of having an interstitial tritium in the lattice, which has been calculated to range from 0.17 eV in Al8Cr5-HT to 0.89 eV in Cr3Si. A comparison of the results from this study with previous theoretical works investigating tritium behavior in other Fe-Al phases identified in the aluminide coating, suggests that metal vacancies are generally able to trap tritium in various Fe-Al aluminide phases. Especially, it was found that Si and Al vacancies would be the most efficient to trap for tritium, followed by Cr vacancies, then Fe and Ni vacancies. In the four Cr-based material phases investigated in this work, it is interesting to note that, in the absences of Fe or Ni species, strong interactions between tritium and the metal vacancies are always occurring by the formation of preferential Cr—T bonds (i.e., no Si—T or Al—T bonds were formed).

Sassi, Michel J.↗
DOE OSTI2021

CHARGED POINT DEFECTS AND DEFECT COMPLEXES IN PHOSPHORENE AND STRUCTURAL PREDICTION OF TAU-11, A DENSITY FUNCTIONAL THEORY APPROACH

With the advent of powerful computers, first principle calculations have become a robust and reliable tool to complement experiments in the study of materials. First principle calculations such as density functional theory can be used to guide experiments to save time and effort when it comes to new and advanced materials discovery. Study of charge defects in materials and crystal structure prediction are such areas in which density functional theory calculations facilitate the prediction of defect formation energy, charge transition levels and elemental site occupations. Experimentally, studying charge defects requires inferring results from various techniques. Likewise, determining site occupancies for similar elements using experimental techniques is challenging and sometimes impossible. In this work, we study the charged intrinsic and extrinsic defects and defect complexes in phosphorene and determine the site occupancies of Al, Fe and Si in the τ11 phase using density functional theory. In the defect work, we calculate the defect formation energy, charge transition level, binding energies and Stokes shift for vacancy, dopant substitution and dopant-vacancy defect complexes in phosphorene. We found that vacancy defect in phosphorene becomes negatively charged in n-type doping and may passivate the dopants and reduce carrier concentration and mobility. For non-metal dopants in phosphorene, we predict that O, S and Si prefer to form dopant-vacancy complexes removing the vacancy defect states from the band gap. Mn dopant-vacancy defect complex exhibits possibility of switching between two magnetic spin states. Lastly, using density functional theory, we complement the results from neutron powder diffraction to determine the site occupancies of Al and Si in τ11 phase which was used to determine the alloying element to develop a high temperature and low density alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of environmental screening and strain on optoelectronic properties of two-dimensional quantum defects

Point defects in hexagonal boron nitride (hBN) are promising candidates as single-photon emitters (SPEs) in nanophotonics and quantum information applications. The precise control of SPEs requires in-depth understanding of their optoelectronic properties. However, how the surrounding environment of host materials, including the number of layers, substrates, and strain, influences SPEs has not been fully understood. Here, in this work, we study the dielectric screening effect due to the number of layers and substrates, and the strain effect on the optical properties of carbon dimer and nitrogen vacancy defects in hBN from first-principles many-body perturbation theory. We report that environmental screening causes a lowering of the quasiparticle gap and exciton binding energy, leading to nearly constant optical excitation energy and exciton radiative lifetime. We explain the results with an analytical model starting from the Bethe–Salpeter equation Hamiltonian with Wannier basis. We also show that optical properties of quantum defects are largely tunable by strain with highly anisotropic response, in good agreement with experimental measurements. Our work clarifies the effect of environmental screening and strain on optoelectronic properties of quantum defects in two-dimensional insulators, facilitating future applications of SPEs and spin qubits in low-dimensional systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Simulating charged defects at database scale

Point defects have a strong influence on the physical properties of materials, often dominating the electronic and optical behavior in semiconductors and insulators. The simulation and analysis of point defects is, therefore, crucial for understanding the growth and operation of materials, especially for optoelectronics applications. In this work, we present a general-purpose Python framework for the analysis of point defects in crystalline materials as well as a generalized workflow for their treatment with high-throughput simulations. The distinguishing feature of our approach is an emphasis on a unique, unit cell, structure-only, definition of point defects which decouples the defect definition, and the specific supercell representation used to simulate the defect. This allows the results of first-principles calculations to be aggregated into a database without extensive provenance information and is a crucial step in building a persistent database of point defects that can grow over time, a key component toward realizing the idea of a “defect genome” that can yield more complex relationships governing the behavior of defects in materials. We demonstrate several examples of the approach for three technologically relevant materials and highlight current pitfalls that must be considered when employing these methodologies as well as their potential solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Atomistic Probing of Defect-Engineered 2H-MoTe 2 Monolayers

Point defects dictate various physical, chemical, and optoelectronic properties of two-dimensional (2D) materials, and therefore, a rudimentary understanding of the formation and spatial distribution of point defects is a key to advancement in 2D material-based nanotechnology. In this work, we performed the demonstration to directly probe the point defects in 2H-MoTe 2 monolayers that are tactically exposed to (i) 200 °C-vacuum-annealing and (ii) 532 nm-laser-illumination; and accordingly, we utilize a deep learning algorithm to classify and quantify the generated point defects. We discovered that tellurium-related defects are mainly generated in both 2H-MoTe 2 samples; but interestingly, 200 °C-vacuum-annealing and 532 nm-laser-illumination modulate a strong n-type and strong p-type 2H-MoTe 2 , respectively. While 200 °C-vacuum-annealing generates tellurium vacancies or tellurium adatoms, 532 nm-laser-illumination prompts oxygen atoms to be adsorbed/chemisorbed at tellurium vacancies, giving rise to the p-type characteristic. This work significantly advances the current understanding of point defect engineering in 2H-MoTe 2 monolayers and other 2D materials, which is critical for developing nanoscale devices with desired functionality.

2H-MoTe 2↗
DOE OSTI2021

Discrete stochastic model of point defect-dislocation interaction for simulating dislocation climb

Dislocation climb is an important high temperature process in metals plasticity, responsible for the phenomena such as creep, swelling, or hardening. Climb is defined by the ability of dislocations to leave their original glide plane by interacting with point defects. As such, dislocation climb is controlled by point defect diffusion/absorption/emission, all of which involve thermal activation. The existing thermodynamically consistent models for climb are generally formulated in a continuum framework, through the definition of effective defect fluxes and climb propensities in response to thermodynamic driving forces. However, the point-wise discrete nature of vacancies (and/or self-interstitials) confers a highly discrete nature to the climb dynamics, which is also strongly affected by elastic forces. The combination of discreteness, thermal activation, and elasticity makes this process too challenging for direct atomistic methods such as molecular dynamics. Here we develop a kinetic Monte Carlo model that captures vacancy generation and transport kinetics acting in conjuction with the evolving elastic fields provided by discrete dislocation dynamics simulations. The two models are coupled via the applied stresses and stress gradients generated by dislocation structures at vacancy locations. Our simulations reveal two surprising results. First that climb is dominated by vacancy emission even when the background vacancy concentration is much higher than the equilibrium one. And, second, that climb velocities might be much faster than otherwise believed when one uses the classical theories of climb. These effects are due to the locality of vacancy-dislocation processes, which are not captured in classical treatments that assume smooth vacancy fluxes and homogeneous concentrations. We apply the method to study elementary climb processes in body-centered cubic iron and furnish climb mobility functions to be used in parametric dislocation dynamics and/or crystal plasticity simulations. Here, we apply the technique to study non-conservative plastic bypass of spherical precipitates by edge dislocations and point out the differences between our discrete approach and existing continuum formulations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Vacancy complexes in Cd 3 As 2

Epitaxial growth of the three-dimensional topological semimetal Cd 3 As 2 on semiconductor substrates enables its use and integration in device applications. Epitaxy also provides an avenue for varying and controlling point defects through modification of the chemical potential during growth. In turn, knowledge of the point defects that are generated in Cd 3 As 2 epilayers will aid the interpretation of electron transport behavior and guide growth efforts to produce material with low defect densities. Point defects in Cd 3 As 2 epilayers grown by molecular beam epitaxy with varying As/Cd flux ratios are probed by positron annihilation spectroscopy. We find that lower As/Cd flux ratios produce higher concentrations of point defects. Remarkably, the measurements indicate that the average defect size is larger than a monovacancy. The data presented here contribute to an evolving picture of vacancy point defects in Cd 3 As 2 and can be used to direct future investigation of the defect-transport relationships in this emerging electronic material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cluster dynamics modeling of irradiation growth in single crystal Zr

Irradiation growth of single-crystal Zr is modeled with a reduced set of cluster dynamics equations. Nucleation and growth of basal and prismatic dislocation loops are both accounted for in the model. Equations are developed for the time evolution of single point defects, a limited number of point defect clusters, interstitial and vacancy loop nucleation rates, as well as for the growth of vacancy loops on basal planes and interstitial loops on prismatic planes. Reduction of the usual infinite hierarchy of cluster dynamics equations to the simple set studied here is justified on the physical basis of the stability of small loops once nucleated. This simplified cluster dynamics model with a small number of adjustable parameters avoids the complexity of explicit representation of higher order point defect clusters. The model shows consistency with experimental observations of the following aspects: (1) the growth rates of Zr crystals along the a- and c-axes; (2) the onset dose for breakaway irradiation growth; (3) the saturation dislocation loop densities of vacancy and interstitial loops; (4) the effects of cold work and temperature on irradiation growth.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Defect modeling in semiconductors: the role of first principles simulations and machine learning

Abstract Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of ‘DFT+ML’ promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2023

Analysis of position-dependent cavity parameters in irradiated metals to obtain insight on fundamental defect migration phenomena

Motion of point defects is a fundamental process that governs microstructure and properties of materials. Here, we examine the near-surface and grain boundary cavity swelling depth profiles in neutron- and ion-irradiated simple metals (Cu, Ni, and Fe-Cr) and investigate diffusional broadening of implanted Ni ions in Fe-Cr alloys. Vacancy migration energies and radiation-enhanced diffusion were experimentally estimated. Cavity denuded zone widths near planar sinks are shown to be dependent on temperature, damage rate, balance of point defects and sinks, and vacancy migration energies. An enhanced cavity swelling zone adjacent to the void-denuded zone was observed in specimens with low to moderate sink strength and interpreted as evidence of 1D gliding interstitial clusters. Radiation-enhanced diffusional broadening of implanted Ni ions (at 400–550 °C) in Fe and Fe-Cr was up to 250 nm toward the surface, which leads to a much broader near-peak-damage region where cavity swelling is suppressed. Diffusional broadening of implanted ions is calculated to be similarly pronounced for self-ion irradiations, particularly near the peak and higher swelling temperature regimes. Adequately high ion energies (~8–15 MeV) are recommended for ion-irradiation studies to provide a sufficiently broad midrange safe analysis region with minimized surface and injected ion effects.

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

Defect Thermodynamics and Transport Properties of Proton Conducting Perovskite Electrode and Electrolyte Materials Evaluated Based on Density Functional Theory Modeling

Both electron-rich and electron-poor perovskite oxides have been used in solid oxide cell applications as electrode and electrolyte materials. The rich oxygen defect chemistry and its coupling to temperature, hydrogen-steam or oxygen-steam gas pressure, or to the applied potentials creates enormous complexities for modeling performance and degradation of the materials. Herein, density functional theory-based thermodynamic modeling was carried out to describe the defect chemistry and transport properties of the proton-conducting electrolyte BaZr1-xYxO3-δ (x≤0.1) and of the triple-conducting perovskite (La,Ba)(Fe,M)O3-δ (M=Y and Zr). The defect thermodynamics of intrinsic point defects and the hydrogen-related defect reactions were solved in integrated defect models and further used to predict the Brouwer diagram and the transport properties of the functional perovskites. For the electron-poor electrolytes BaZr0.9Y0.1O3-δ, the developed model has been used to describe the experimental transport properties in the SOC operating conditions. Specifically, the roles played by the acceptor-bound holes and the intrinsic and hydrogen point defects upon the conductivities of holes, protons, and oxygen vacancies under the hydrogen-rich and oxygen-rich conditions at various humidity levels were demonstrated. A defect modeling tool was also developed for the triple-conducting perovskite (La,Ba)(Fe,M)O3-δ (M=Y and Zr) to examine magnetic effects and hydride defects in defect equilibria.

defect thermodynamics↗
DOE OSTI2022

AlScO 3 perovskite—An ~8 eV bandgap oxide predicted to exhibit low small hole polaron ionization energies and p -type conductivity at elevated temperatures

Here, we investigate electronic structure and dopability of an ultrawide bandgap (UWBG) AlScO 3 perovskite, a known high-pressure and long-lived metastable oxide. From first-principles electronic structure calculations, HSE06(+G 0 W 0 ), we find this material to exhibit an indirect bandgap of around 8.0 eV. Defect calculations point to cation and oxygen vacancies as the dominant intrinsic point defects limiting extrinsic doping. While acceptor behaving Al and Sc vacancies prevent n-type doping, oxygen vacancies permit the Fermi energy to reach ~0.3 eV above the valence band maximum, rendering AlScO 3 p-type dopable. Furthermore, we find that both Mg and Zn could serve as extrinsic p-type dopants. Specifically, Mg is predicted to have achievable net acceptor concentrations of ~10 17 cm -3 with ionization energy of bound small hole polarons of ~0.49 eV and free ones below 0.1 eV. These values place AlScO 3 among the UWBG oxides with lowest bound small hole polaron ionization energies, which, as we find, is likely due to large ionic dielectric constant that correlates well with low hole polaron ionization energies across various UWBG oxides.

14 SOLAR ENERGY↗
DOE OSTI2024

Minimizing the diffusivity difference between vacancies and interstitials in multi-principal element alloys

Interstitial atoms usually diffuse much faster than vacancies, which is often the root cause for the ineffective recombination of point defects in metals under irradiation. Here, via ab initio modeling of single-defect diffusion behavior in the equiatomic NiCoCrFe(Pd) alloy, we demonstrate an alloy design strategy that can reduce the diffusivity difference between the two types of point defects. The two diffusivities become almost equal after substituting the NiCoCrFe base alloy with Pd. The underlying mechanism is that Pd, with a much larger atomic size (hence larger compressibility) than the rest of the constituents, not only heightens the activation energy barrier ( E a ) for interstitial motion by narrowing the diffusion channels but simultaneously also reduces E a for vacancies due to less energy penalty required for bond length change between the initial and the saddle states. Our findings have a broad implication that the dynamics of point defects can be manipulated by taking advantage of the atomic size disparity, to facilitate point-defect annihilation that suppresses void formation and swelling, thereby improving radiation tolerance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Implications of doping on microstructure, processing, and thermoelectric performance: The case of PbSe

Abstract In this work, we highlight the often-overlooked effects of doping on the microstructure and performance of bulk thermoelectric materials to offer a broader perspective on how dopants interact with their parent material. Using PbSe doped with Na, Ag, and K as a model material system, we combine original computational, experimental, and microscopy data with established trends in material behavior, to provide an in-depth discussion of the relationship between dopants, processing, and microstructure, and their effects on thermoelectric efficiency and thermal stability. Notable observations include differences in the microstructure and mass loss of thermally treated samples of Na- and Ag-doped PbSe, as well as findings that Na and K cations exist predominantly as substitutional point defects while Ag also occupies interstitial sites and exhibits lower solubility. We discuss how these differences in point defect populations are known to affect a dopants’ ability to alter carrier concentration and how they may affect the mechanical properties of PbSe during processing. Graphic Abstract

Grovogui, Jann A. (ORCID:0000000200481049)↗
DOE OSTI2022

Machine learning pipeline to predict defect behavior in metallic alloy systems

The interaction between defect and solute atoms is critical to the thermodynamic and kinetic behavior of metallic alloys under exposure to high-energy radiation, causing irradiation damage in materials. Radiation can generate non-equilibrium concentrations of point defects such as vacancies and interstitials. The excess point defects not only accelerate diffusional processes such as precipitation that cause radiation embrittlement, but also change the pathway of phase transformations, including nucleation processes. Understanding these defect behaviors is complicated by the challenge and complexity of addressing each possible local and discrete distribution of environments and chemical interactions around targeted defects-solute or solute-solute complexes. To resolve the challenge, machine learning regression techniques have emerged as powerful tools that can train and construct an energy model to accurately describe the chemical interactions of solutes and defects. In Fiscal Year 2022, the work focused on the workflow development and demonstration using machine learning regression, density functional theory, cluster expansion, and Monte Carlo simulation to predict the effects of ternary solute elements (e.g., aluminum and molybdenum) and point defects on the Cr-rich $\alpha^{\prime}$ precipitation in multicomponent FeCr model alloys. The computational outcomes include the prediction of the ternary phase diagram, vacancy formation energy for different compositions, and the effect of vacancies on the nucleation of Cr-rich clusters. The simulations predict a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from $\alpha^{\prime}$ precipitates. Additionally, the simulations show the formation of Cr-vacancy clusters as the initial nuclei for stable nucleation and growth of $\alpha^{\prime}$ particles. The results demonstrate important outcomes and applications of using machine learning pipeline to study model or commercial alloys with multicomponent solute species and point defects.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Intrinsic Defect-Induced Local Semiconducting-to-Metallic Regions Within Monolayer 1T-TiS2 Displayed by First-Principles Calculations and Scanning Tunneling Microscopy

Using density functional theory (DFT) and scanning tunneling microscopy (STM), the intrinsic point defects, formation energy, and electronic structure of 1T-TiS2 were investigated. Defect systems include single-atom vacancies, interstitial and adatom additions, and direct atomic substitution. Using a collective approach for analyzing realistic systems for point defect investigation, we provide a more straightforward comparison to the experimental measurements, reproducing more realistic environmental conditions related to thin film growth. STM images are compared to computationally simulated electron density images to identify specific geometries that result from favorable point defects. DFT suggests that titanium interstitials are the most energetically favorable intrinsic defect, and sulfur vacancies are more likely to form than titanium vacancies within this realistic analysis, which is in agreement with STM data. A pristine, stoichiometric monolayer system is calculated to have a direct band gap of 0.422 eV, which varies based on local point defects. Local semiconducting-to-metallic electronic transitions are predicted to occur based on the presence of Ti interstitials.

Keeney, P. J.↗
DOE OSTI2024

Atomistic Modeling of Irradiation-Induced Defects and Clusters in Additively-Manufactured Austenitic Stainless Steel

Understanding the progression of irradiation-induced defects in real materials and alloys requires insight into the interaction between point defects, defect clusters, and the existing microstructure. This is particularly true when it comes to additively manufactured (AM) materials with complicated microstructures and residual stresses. In this work, we employ molecular dynamics (MD) to simulate the creation of point defects via primary radiation damage modeling and point defect interactions with extended defects such as voids and dislocations using simplified but representative alloy compositions. We analyze the effect of alloying elements on the formation energies of the defect clusters and measure interactions such as sink strengths and binding energies. This study provides insight into the unique behaviors of irradiation damage in AM materials and information necessary to bridge the gap in length and time scales to connect the atomistic behavior of defects to longer length scale simulations such as cluster dynamics.

36 MATERIALS SCIENCE↗