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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Simulated effect of defect volume and location on very high cycle fatigue of laser beam powder bed fused AlSi10Mg

This study quantifies the interaction between volumetric defect location and size on the very high cycle fatigue (VHCF) of laser beam powder bed fused (LB-PBF) AlSi10Mg. Crystal plasticity finite element method (CPFEM) simulations were used to investigate the effects of defect location and size on the driving force for crack initiation. The CPFEM model was calibrated against uniaxial and cyclic experimental data of LB-PBF AlSi10Mg. Defect characteristics were informed by experimental data from the specimens produced in various geometries to create realistic representative volume elements (RVEs) with equivalent volume fractions of defects. By embedding defects of varying sizes and locations within the RVEs, fatigue indicator parameters (FIPs) were calculated to analyze the impact of defects’ characteristics on fatigue performance. Different combinations of defect volume and locations were generated for various microstructure instantiations, providing insight into extreme value fatigue responses. Larger defect volumes located on free surfaces consistently generated the highest FIPs, suggesting defect size and boundary proximity intensify stress concentration effects. RVEs with multiple smaller defects produced lower FIPs than those with single large critical defects. These findings underscore the critical role of defect characteristics on fatigue life, providing a foundation for future predictive modeling in fatigue-sensitive AM applications.

AlSi10Mg↗
DOE OSTI2025

Optimization of edge bead removal (EBR) process to enhance defect reduction in optical lithography

Defect reduction remains a critical objective in the integrated circuit manufacturing process, particularly within the highly re-entrant lithography modules where minimizing defects is crucial. Defects at the wafer edge can contaminate lithography modules and downstream processing equipment, leading to redistribution onto the wafer surface and adversely affecting overall device yield. A persistent challenge in the resist coating process is the formation of resist edge beads, driven by the strong Van der Waals attraction of excess photoresist (PR) to itself and the underlying substrate. The edge bead removal (EBR) process is a standard cleaning step designed to eliminate these edge beads and prevent potential contamination. Here, in this study, we identify the sources of EBR induced defects and additional EBR process encroachment toward edge patterning during the EBR cleaning process. This study provides a comprehensive study aimed at optimizing the EBR cleaning process to effectively eliminate EBR-induced defects, thereby enhancing overall device yield. Specifically, we identify three primary defects induced by the EBR cleaning process: rainbow-type, finger-shaped, and teardrop-type defects. Our experimental study reveals that in addition to EBR rinse time, PR cast time is crucial parameters contributing to the formation of these defects. By properly optimizing the PR cast time and EBR rinse time, we were able to remove nearly 100 % of dense clusters of defects that were easily visible even at low magnification optical microscopy throughout the wafer edge. We observed that shorter PR casting times shows edge defects caused by inefficient EBR process because of insufficient time for PR to fully settle causing superfluous PR to continue flowing toward wafer edge during EBR clearing step, leading to partial removal of PR at the wafer edge and the formation of rainbow defects. Proper optimization of both PR casting time and EBR chemistries dispense time is essential to resolve these defects, ensuring efficient EBR cleaning process and improved overall device yield.

42 ENGINEERING↗
DOE OSTI2025

Energy Barriers for H 2 Uptake and Release by Defects in Boron Nitride

Density functional theory simulations (DFT) have been used in combination with the climbing image nudged elastic band method (CI-NEB) to determine the energy barriers required to dissociate H 2 at four types of h-BN defects and evaluate the potential for reacted H species to hop to adjacent available sites. The defects were selected based on previous work in which ab initio thermodynamics was used to identify specific defects with optimal thermodynamics for reversible H 2 storage, that is, the defects would dissociate molecular H 2 while not binding H species too strongly so that hydrogen release is favorable. The four defects investigated are the N and B monovacancies, which only offer either B or N defective sites respectively, as well as larger cluster vacancy defects named 3V(1B2N) and hexagonal 6V(3B3N), which present a mixture of B and N defect sites available for H binding. While homogeneous H 2 dissociation by B and N monovacancies have energy barriers larger than 0.4 eV, we found that mixed BN terminated defects have energy barrier lower than 0.4 eV for H 2 dissociation. In particular, H 2 dissociation by the hexagonal 6V(3B3N) defect, exclusively made of Frustrated Lewis Pairs (FLPs), is almost barrierless. The energy barrier for subsequent H hopping to available adjacent B or N sites of the defect were also calculated. It was found that the hopping of H species generally involves high energy barriers ranging from 0.26 eV to 2.57 eV. In conclusion, this work completes previous thermodynamic theoretical investigations and provides a more comprehensive picture of H 2 reaction with defective h-BN for potential H 2 storage and recovery.

Sassi, Michel J. [Pacific Northwest National Labor↗
DOE OSTI2017

Quasiparticle and hybrid density functional methods in defect studies: An application to the nitrogen vacancy in GaN

Defects in semiconductors can play a vital role in the performance of electronic devices, with native defects often dominating the electronic properties of the semiconductor. Understanding the relationship between structural defects and electronic function will be central to the design of new high-performance materials. In particular, it is necessary to quantitatively understand the energy and lifetime of electronic states associated with the defect. In this work, we apply first-principles density functional theory (DFT) and many-body perturbation theory within the GW approximation to understand the nature and energy of the defect states associated with a charged nitrogen vacancy on the electronic properties of gallium nitride (GaN), as a model of a well-studied and important wide gap semiconductor grown with defects. We systematically investigate the sources of error associated with the GW approximation and the role of the underlying atomic structure on the predicted defect state energies. Additionally, analysis of the computed electronic density of states (DOS) reveals that there is one occupied defect state 0.2 eV below the valence band maximum and three unoccupied defect states at energy of 0.2–0.4 eV above the conduction band minimum, suggesting that this defect in the +1 charge state will not behave as a carrier trap. Furthermore, we compare the character and energy of the defect state obtained from GW and DFT using the HSE approximate density functional and find excellent agreement. This systematic study provides a more complete understanding of how to obtain quantitative defect energy states in bulk semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

Defects in electro-optically active polymer solids

There is considerable current interest in the application of organic and polymeric materials for electronic and photonic devices. The rapid, non-linear optical (NLO) response of these materials makes them attractive candidates for waveguides, interferometers, and frequency doublers. In order to realize the full potential of these systems, it is necessary to develop processing schemes which can fabricate these molecules into ordered arrangements. There is enormous potential for introducing well-defined, local variations in microstructure to control the photonic properties of organic materials by rational 'defect engineering.' This effort may eventually become as technologically important as the manipulation of the electronic structure of solid-state silicon based devices is at present. The success of this endeavor will require complimentary efforts in the synthesis, processing, and characterization of new materials. Detailed information about local microstructure will be necessary to understand the influence of symmetry breaking of the solid phases near point, line, and planar defects. In metallic and inorganic polycrystalline materials, defects play an important role in modifying macroscopic properties. To understand the influence of particular defects on the properties of materials, it has proven useful to isolate the defect by creating bicrystals between two-component single crystals. In this way the geometry of a grain boundary defect and its effect on macroscopic properties can be determined unambiguously. In crystalline polymers it would be valuable to establish a similar depth of understanding about the relationship between defect structure and macroscopic properties. Conventionally processed crystalline polymers have small crystallites (10-20 nm), which implies a large defect density in the solid state. Although this means that defects may play an important or even dominant role in crystalline or liquid crystalline polymer systems, it also makes it difficult to isolate the effect of a particular boundary on a macroscopically observed property. However, the development of solid-state and thin-film polymerization mechanisms have facilitated the synthesis of highly organized and ordered polymers. These systems provide a unique opportunity to isolate and investigate in detail the structure of covalently bonded solids near defects and the effect of these defects on the properties of the material. The study of defects in solid polymers has been the subject of a recent review (Martin, 1993).

Martin, David C.↗
DOE OSTI2019

Temperature-dependent defect accumulation and evolution in Ni-irradiated NiFe concentrated solid-solution alloy

Temperature significantly affects defect migration and evolution in irradiated materials. However, the effects of temperature on defect evolution in concentrated solid-solution alloys (CSAs), including high entropy alloys, are not well understood, despite their potential as structural materials in advanced nuclear reactors. As an important model system of these CSAs, equiatomic Ni 50 Fe 50 (NiFe) was selected to understand the effects of temperature on defect evolution during irradiation and subsequent thermal annealing. Specifically, defect accumulation and evolution in NiFe alloy under Ni-ion irradiation at 150, 300, and 500 K were studied, and the irradiated specimens were subsequently annealed at higher temperatures. Rutherford backscattering spectrometry along the <100> channeling direction was employed to study damage accumulation and evolution before and after each irradiation and annealing experiment. Here we show that more defects survive and accumulate at 150 K, but more importantly defects can migrate to deeper depths at this low irradiation temperature. Irradiation-induced damage at 150 and 300 K does not recover substantially after post-irradiation annealing at 500 K, but dramatic recovery is observed after post-irradiation annealing at 700 K, indicating an onset temperature of defect recovery between 500 and 700 K. Here, the migration of irradiation-induced defects upon annealing is closely related to the mobility and stress state arising from the surviving defects. With the consideration of five stages of defect recovery in conventional dilute alloys, the underlying mechanisms for temperature-dependent defect accumulation and evolution in NiFe CSA are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS 2 , we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS 2 are discussed. In this study, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Defect Energetics in Pseudo-Cubic Mixed Halide Lead Perovskites from First-Principles

Owing to the increasing popularity of lead-based hybrid perovskites for photovoltaic (PV) applications, it is crucial to understand their defect energetics and its influence on their optoelectronic properties. In this work, we simulate various point defects in pseudocubic structures of mixed iodide-bromide and bromide-chloride methylammonium lead perovskites with the general formula MAPbI 3-y Br y or MAPbBr 3-y Cl y (where y is between 0 and 3), and use first-principles based density functional theory computations to study their relative formation energies and charge transition levels. We identify vacancy defects and Pb on MA antisite defect as the lowest energy native defects in each perovskite. Here, we observe that while the low energy defects in all MAPbI 3-y Br y systems only create shallow transition levels, the Br or Cl vacancy defects in the Cl-containing pervoskites have low energy and form deep levels which become deeper for higher Cl content. We examine the structures and density of states of pure and defect-containing perovskite systems to obtain an understanding of the nature of defect levels. Further, we study extrinsic substitution by different elements at the Pb site in MAPbBr 3 , MAPbCl 3 , and the 50-50 mixed halide perovskite, MAPbBr 1.5 Cl 1.5 , and identify some transition metals that create lower energy defects than the dominant intrinsic defects and also create midgap charge transition levels.

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

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Abstract Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types.

Materials Science↗
DOE OSTI2025

First-Principles Study of Molecular Hydrogen Activation by Defects in Boron Nitride

Here, we used density functional theory simulations in combination with ab initio thermodynamics to determine the H 2 partial pressure (p H2 )-dependent energetics associated with H 2 activation and recovery at various defect sites in hexagonal boron nitride (h-BN). We found that some defects are very reactive with hydrogen, thereby definitely trapping hydrogen in defective h-BN. However, depending on hydrogen partial pressure, less reactive defect sites can be populated. Because of the lower binding capability of these sites, they would allow hydrogen to be recycled and recovered. For small defect sizes, we found that hydrogen preferentially binds to nitrogen sites by forming N–H bonds, and if no N sites are available then boron sites would be the next to bind hydrogen. Hydrogen dissociation via frustrated Lewis pair is found to be more favorable than forming only N–H bonds but only if the defect size is large enough to accommodate steric effects. For specific conditions such as T = 400 K, p H2 = 1 bar, and only considering one molecular H 2 per defect, three defects, namely, the N monovacancy, 3V(1B2N), and hexagonal 6V(3B3N) could play a role in both the activation and recycling of H 2 as they would be reacting enough to allow a favorable splitting of H 2 while not binding too strongly to allow its recovery. More broadly, a range of p H2 and hydrogen loading conditions were investigated for different types of defects and the finding suggests that p H2 could be used to fine-tune the Gibbs free energy of hydrogenation, thereby allowing several types of defects at different hydrogen loading contents to play a role in the activation/recovery process of H 2 in defective h-BN.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

The critical role of intrinsic defects and many-body interactions on the stability of MnBi2Te4

Intrinsic antisite defects pose a major challenge to understanding and predicting the exotic properties of the layered topological magnetic insulator MnBi2Te4 (MBT). In this work, we study the origin of the abundance of intrinsic defects in MBT, including many-body defect–defect interactions and many-body electronic correlations. Until now, ab initio methods have struggled to explain thermodynamic stability and properties influenced by defect behavior in MBT. We model native Mn–Bi antisite defects in MBT at finite temperatures using a cluster expansion that includes defect–defect interactions. To overcome the limitations of conventional density functional theory (DFT), we introduce a hybrid approach that incorporates high-accuracy quantum Monte Carlo (QMC) calculations, introducing missing correlations. This strategy allows for accurate estimation of defect energetics and finite-temperature properties. We compute the configurational free energy, defect concentration, and configurational heat capacity, revealing a second-order order–disorder phase transition near the experimental synthesis temperature. Our study provides the first theoretical insight into the thermodynamics of intrinsic defects in MBT. The negative free energy relative to pristine MBT at synthesis temperatures indicates that Mn–Bi antisite formation is thermodynamically spontaneous. We also present a broadly applicable general framework for correcting low-level theoretical theories using highly accurate many-body corrections from QMC.

Ghaffar, Abdul [ORNL] (ORCID:0000000241190168)↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Carrier recombination mechanism at defects in wide band gap two-dimensional materials from first principles

The identification and design of defects in two-dimensional (2D) materials as promising single photon emitters (SPEs) requires a deep understanding of the underlying carrier recombination mechanisms. Yet, the dominant mechanism of carrier recombination at defects in 2D materials has not been well understood, and some outstanding questions remain: How do recombination processes at defects differ between 2D and 3D systems? What factors determine defects in 2D materials as excellent SPEs at room temperature? In order to address these questions, we developed first-principles methods to accurately calculate the radiative and nonradiative recombination rates at defects in 2D materials, using h-BN as a prototypical example. In this work, we reveal the carrier recombination mechanism at defects in 2D materials being mostly dominated by defect-defect state recombination in contrast to defect-bulk state recombination in most 3D semiconductors. In particular, we disentangle the nonradiative recombination mechanism into key physical quantities: the zero-phonon line and Huang-Rhys factor. At the end, we identified that strain can effectively tune the electron-phonon coupling at defect centers and drastically change the nonradiative recombination rates. Our theoretical development serves as a general platform for understanding carrier recombination at defects in 2D materials, while providing pathways for engineering of quantum efficiency of SPEs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Defect reaction network in Si-doped InAs. Numerical predictions.

This Report characterizes the defects in the defect reaction network in silicon-doped, n-type InAs predicted with first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si-doped InAs, until culminating in immobile reaction products. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon-related defects in the reaction network. This Report serves to extend the results for the properties of intrinsic defects in bulk InAs as collated in SAND 2013 - 2477: ''Simple intrinsic defects in InAs : numerical predictions'' to include Si-containing simple defects likely to be present in a radiation-induced defect reaction sequence.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Investigation of Defect Physics for Efficient, Durable and Ubiquitous Perovskite Solar Modules (Final Technical Report)

Organometal halide perovskite solar cells have experienced eye-catching improvements in its recent few years. It serves as one of the most promising candidates to replace the currently widely used silicon-based solar modules. To implement its final step to the real application, functional longevity becomes the dernier continent to conquer. As a polycrystalline material, defect plays critical role in the efficiency and stability of the perovskite solar cells. Thus, the investigation of the defect physics of the perovskite layer is indispensable in this research field. However, hard evidence and a consensus are still lacking in terms of the specific nature of the defects and their effects on performance and hysteresis, and perhaps even more importantly, there is absence of fundamental understanding of the correlations between the defects and long-term operational stability of the device. A more fundamental understanding of the nature of defects in perovskite materials is of paramount importance to progress their efficiency and durability. In this work we propose in-depth studies of correlations of defects with performance and stability of perovskite solar cells. Our project aimed 1) to investigate the defects physics in perovskite solar cells, and 2) to develop a comprehensive understanding and physical model of defects and its influence on performance and stability of perovskite solar cells. With the support from program manager, Peter Lobaccaro, and the Solar Energy Technologies Office of U.S. Department of Energy, the project ends with impact achievements. Our research results have systematically provided strategies to analyze the influences of constructive molecular configurations to the charged defects in the perovskite lattices and developed in-depth understanding of chemical additive approach to improve the perovskite solar cell performance and stability. As history has shown us, control over defect properties of semiconductor materials is the key to achieving high performance and low cost devices. Therefore, the potential impact of unlocking the understanding and manipulation of defects in perovskites is great, enabling this technology to realize SETO goals. The research project is highly productive with 18 published papers in three years in top-level journals such as Science, Nature, Nature Materials, Nature Communications, Journal of American Chemistry Society, Joule, Advanced Materials, and Nano Letters. These works have drawn great attention nationwide with notable total citations over 700 times from 2020 to 2022.

14 SOLAR ENERGY↗
NASA NTRS2018

Automated Fiber Placement Defect Identity Cards: Cause, Anticipation, Existence, Significance, and Progression

Automated Fiber Placement (AFP), a major composite manufacturing process, can result in many defects during the layup process that often require manual corrective action to produce a part with acceptable quality. These defects are the main limitation of the technology and can be hard to categorize or define in many situations. This paper provides a thorough definition and classification of all AFP defects. This effort constitutes a comprehensive and extensive library relevant to AFP defects. The defects selected and defined in this work are based on understanding and experience from the manufacture and research of advanced composite structure. Proper classification of these defects required an in-depth literature review and consideration of various viewpoints ranging from designers, manufacturers, analysts, and inspection professionals. Collectively, these sources were utilized to develop the most accurate view of each of the individual defect types. The results are presented as identity cards for each defect type, intended to provide researchers and the manufacturing industry a clear understanding of the (1) cause, (2) anticipation, (3) existence, (4) significance, and (5) progression of the defined AFP defects. The link between AFP defects and process planning, layup strategies, and machining was also investigated. Categorization of all important automated fiber placement defects is presented.

Harik, Ramy↗
DOE OSTI2024

Relating Aerial Infrared Thermography Defects to Photovoltaic Performance: Preprint

In this research, we examine the relationship between aerial IR defect analysis and photovoltaic (PV) performance data for twelve utility- and commercial-scale solar sites in the United States. To do this, we fuse the site diagram geoJSON's, aerial infrared thermography (aIRT) defect analyses, and associated inverter time series, allowing for a direct comparison between site defects and time series data. Defect analyses were provided by Zeitview, under its Solar Insights platform. Following the data fusion process, we look at the relationship between system performance and aIRT defects. We investigate the relationship between degradation and hotspot defects, as well as the relationship between AC power data and offline strings and misaligned modules. In general, system degradation was not affected by long-term or balance-of-system (BoS) defects as they occurred infrequently in the data set. However, for one system, a near statistically significant relationship (p-value=0.057) was found when comparing the degradation of inverter blocks with several multi-hotspot defects to all other inverter blocks without this particular defect. There was strong alignment when comparing short-term recoverable module defects such as stuck trackers and offline strings to time series data. In general, we found that when an inverter block has more than 80% of modules flagged for one of these defects, its AC power time data is flat-lined and the inverter block is not producing.

aerial inspection↗
DOE OSTI2017

Defect Genome of Cubic Perovskites for Fuel Cell Applications

Heterogeneities such as point defects, inherent to material systems, can profoundly influence material functionalities critical for numerous energy applications. This influence in principle can be identified and quantified through development of large defect data sets which we call the defect genome, employing high-throughput ab initio calculations. However, high-throughput screening of material models with point defects dramatically increases the computational complexity and chemical search space, creating major impediments toward developing a defect genome. In this paper, we overcome these impediments by employing computationally tractable ab initio models driven by highly scalable workflows, to study formation and interaction of various point defects (e.g., O vacancies, H interstitials, and Y substitutional dopant), in over 80 cubic perovskites, for potential proton-conducting ceramic fuel cell (PCFC) applications. The resulting defect data sets identify several promising perovskite compounds that can exhibit high proton conductivity. Furthermore, the data sets also enable us to identify and explain, insightful and novel correlations among defect energies, material identities, and defect-induced local structural distortions. Finally, such defect data sets and resultant correlations are necessary to build statistical machine learning models, which are required to accelerate discovery of new materials.

30 DIRECT ENERGY CONVERSION↗