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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reversible Binding Interfaces Made of Microstructured Polymer Brushes

The polymer brush architecture of the end-tethered polymer molecules is one of the most widely used efficient methods to regulate interfacial interactions in colloidal systems found in live matter and manufactured materials. Emerging applications of polymer brush structures require solutions to new tasks in the control of interfacial interactions. The rapid development of live cell manufacturing relies on scalable and efficient cell harvesting methods. Stimuli-responsive surfaces made of surface-grafted poly(N-isopropylacrylamide) (PNIPAM) can bind and detach the adherent cell upon changes in temperature and have been used for cell growth and harvesting. The applications are limited by the requirement to satisfy a range of PNIPAM coating characteristics that depend on the dimensions of the integrin complex in the cell membrane and the basal surface. The analysis of the microstructured surfaces, when adhesive and disjoining functions of the microdomains are decoupled, shows that many limitations of PNIPAM one-component coatings can be avoided by using a much broader range of structural characteristics of the microstructured interfaces composed of alternating disjoining PNIPAM domains and adhesive polymeric domains with cell-affinity functional groups. Temperature-controlled reversible adhesion to such microstructured interfaces is studied here experimentally with model systems of solid spherical particles and by employing simulations for solid and soft membranes interacting with the microstructured surfaces to mimic interactions with soft and solid disk-like particles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Examination of 3013 Containers Baseline Surface Features

The Surveillance and Monitoring Program at Los Alamos National Laboratory (LANL) was tasked with evaluating the baseline features of 3013 containers. This baseline is to be used as a basis for comparison for 3013 containers that had been packaged with corrosive plutonium materials. The LANL team evaluated an unwelded container, an unused welded container, and a welded container that had held plutonium metal without any corrosive impurities. These three containers had features with depths no larger than 6 µm and had similar depth distributions. The features observed in the baseline containers were all shallower than those seen in containers packaged with corrosive plutonium materials. This work establishes workflows for feature identification and measurement, as well as establishment of baseline data for future comparison with corroded containers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. Here, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Additive Manufacturing of Fuel Injectors

Additive manufacturing (AM), also known as 3D-printing, has been shifting from a novelty prototyping paradigm to a legitimate manufacturing tool capable of creating components for highly complex engineered products. An emerging AM technology for producing metal parts is the laser powder bed fusion (L-PBF) process; however, industry manufacturing specifications and component design practices for L-PBF have not yet been established. Solar Turbines Incorporated (Solar), an industrial gas turbine manufacturer, has been evaluating AM technology for development and production applications with the desire to enable accelerated product development cycle times, overall turbine efficiency improvements, and supply chain flexibility relative to conventional manufacturing processes (casting, brazing, welding). Accordingly, Solar teamed with EWI on a joint two-and-a-half-year project with the goal of developing a production L-PBF AM process capable of consistently producing high-nickel alloy material suitable for high temperature gas turbine engine fuel injector components. The project plan tasks were designed to understand the interaction of the process variables and their combined impact on the resultant AM material quality. The composition of the high-nickel alloy powders selected for this program met the conventional cast Hastelloy X compositional limits and were commercially available in different particle size distributions (PSD) from two suppliers. Solar produced all the test articles and both EWI and Solar shared responsibility for analyzing them. The effects of powder metal input stock, laser parameters, heat treatments, and post-finishing methods were evaluated. This process knowledge was then used to generate tensile, fatigue, and creep material properties data curves suitable for component design activities. The key process controls for ensuring consistent material properties were documented in AM powder and process specifications. The basic components of the project were: • Powder metal input stock: Powder characterization, dimensional accuracy, metallurgical characterization, and mechanical properties evaluation. • Process parameters: Laser parameter effects, post-printing heat-treatment development, mechanical properties evaluation, and post-finishing technique. • Material design curves: Room and elevated temperature tensiles, low cycle fatigue, and creep rupture properties curves generated. • AM specifications: Key metal powder characteristics, laser parameters, and heat-treatment controls identified.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Signatures of distinct impurity configurations in atomic-resolution valence electron-energy-loss spectroscopy: Application to graphene

The detection and identification of impurities and other point defects in materials is a challenging task. Signatures for point defects are typically obtained using spectroscopies without spatial resolution. Here in this paper, we demonstrate the power of valence electron-energy-loss spectroscopy (VEELS) in an aberration-corrected scanning transmission-electron microscope (STEM) to provide energy-resolved and atomically resolved maps of electronic excitations of individual impurities which, combined with theoretical simulations, yield unique signatures of distinct bonding configurations of impurities. We report VEELS maps for isolated Si impurities in graphene, which are known to exist in two distinct configurations. We also report simulations of the maps, based on density functional theory and dynamical scattering theory, which agree with and provide direct interpretation of observed features. We show that theoretical VEELS maps exhibit distinct and unambiguous signatures for the threefold- and fourfold-coordinated configurations of Si impurities in different energy-loss windows, corresponding to impurity-induced bound states, resonances, and antiresonances. With the advent of new monochromators and detectors with high energy resolution and low signal-to-noise ratio, the present work ushers an atomically resolved STEM-based spectroscopy of individual impurities as an alternative to conventional spectroscopies for probing impurities and defects.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Detector Materials Scoping Study: Research and Development Recommendations

Radiation detection materials have a unique role in many nuclear security missions. The variety of different applications requires the use of different materials whose characteristics are determined by the specific application requirements. While baseline capabilities have been established to use radiation detection in these applications, the potential to greatly advance those capabilities still exists through the development of superior radiation detection materials and/or the development of new materials that enable new technologies to be implemented more effectively. In 2024 the Defense Nuclear Nonproliferation Research and Development (DNN R&D) Near Field Detection Portfolio charged a detector materials scoping study, composed of a group of radiation-detection materials subject matter experts, to produce a community-wide consensus view of the current state of radiation materials research and to set recommendations for expanded investment over a ten-year time frame. This scoping study sought to understand and document the landscape of how radiation detection materials are used, what characteristics drive the selection of various materials, and what developments are needed to drive the state of the art. To respond to this charge, the scoping study created three working groups, each focusing on a different topical area in radiation detection materials research - Semiconductor Materials, Inorganic Scintillators and Organic Scintillators - with the task to identify the current state of the art in materials research and to provide mission-relevant research and development recommendations. Each working group was chaired by two members of the project team with widely recognized expertise in the field. Solicitations were sent out to experts at the DOE national laboratories, industry, academia and federal government agencies to participate in these working groups. The working groups met at regular intervals throughout 2024 and early 2025, each delivering a technical roadmap designed to enable the pursuit of R&D more intensely on high-impact materials challenges and detector solutions that are deemed as the greatest strategic value to DNN R&D and its stakeholders. The roadmap is also intended to help communicate recommended programmatic priorities within DNN R&D and across the nonproliferation and research communities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps and constitutive models simultaneously is currently lacking. Further, this paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Technique development for modulus, microcracking, hermeticity, and coating evaluation capability characterization of SiC/SiC tubes

Driven by the need to enlarge the safety margins of nuclear fission reactors in accident scenarios, research and development of accident-tolerant fuel has become an important topic in the nuclear engineering and materials community. A continuous-fiber SiC/SiC composite is under consideration as a replacement for traditional zirconium alloy cladding owing to its high-temperature stability, chemical inertness, and exceptional irradiation resistance. An important task is the development of characterization techniques for SiC/SiC cladding, since traditional work using rectangular bars or disks cannot directly provide useful information on the properties of SiC/SiC composite tubes for fuel cladding applications. At Oak Ridge National Laboratory, experimental capabilities are under development to characterize the modulus, microcracking, and hermeticity of as-fabricated, as-irradiated SiC/SiC composite tubes. Resonant ultrasound spectroscopy has been validated as a promising technique to evaluate the elastic properties of SiC/SiC composite tubes and microcracking within the material. A similar technique, impulse excitation, is efficient in determining the basic mechanical properties of SiC bars prepared by chemical vapor deposition; it also has potential for application in studying the mechanical properties of SiC/SiC composite tubes. Complete evaluation of the quality of the developed coatings, a major mitigation strategy against gas permeation and hydrothermal corrosion, requires the deployment of various experimental techniques, such as scratch indentation, tensile pulling-off tests, and scanning electron microscopy. In addition, a comprehensive permeation test station is being established to assess the hermeticity of SiC/SiC composite tubes and to determine the H/D/He permeability of SiC/SiC composites. This report summarizes the current status of the development of these experimental capabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

SAM-I-Am: Semantic boosting for zero-shot atomic-scale electron micrograph segmentation

Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics — where are boundaries located? what segments are logically similar? — change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. Here, we apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, serves as a post-processing engine that extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero-shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L).

36 MATERIALS SCIENCE↗
DOE OSTI2018

X-ray magnetic circular dichroism and near-edge X-ray absorption fine structure of buried interfacial magnetism measured by using a scanning tunneling microscope tip

Magnetism at buried interfaces plays a crucial role in many emerging phenomena, but detection of interfacial magnetism in close proximity to a surface with elemental and chemical sensitivity is a challenging task. Here, we use a low temperature synchrotron x-ray scanning tunneling microscopy to investigate x-ray magnetic circular dichroism and near edge x-ray absorption fine structure of La 0.67 Sr 0.33 MnO 3 -LaNiO 3 superlattices in the absence of a magnetic field. In stark contrast to the weak magnetic signal of Mn when the La 0.67 Sr 0.33 MnO 3 layers are located on top, a robust x-ray magnetic circular dichroism signal is detected when they are buried underneath the LaNiO 3 layers. The near edge x-ray absorption fine structure reveals the valence states of the manganese while the oxygen K- edge x-ray absorption spectra show an increase in hole formation indicating a cogent charge transfer at the LaNiO 3 /La 0.67 Sr 0.33 MnO 3 interface. This work demonstrates that scanning tunneling microscopy can be extended to the synchrotron X-ray study of buried interfaces by controlling the sample-tip (e.g., detector) separation in the nanometer regime. The close sample-tip proximity further provides a path toward probing of small lateral areas with zero-field measurements in multi-domain samples.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Arc Parameter Modeling (Arc Breakdown Experiments and Modeling: Confined Geometries and Alternate Gap Materials)

Sandia Nationa.l Laboratories (SNL) and The Boeing Company (Boeing) collaborated on this PTS to develop the capability to predict thermal and electrical characteristics of short arcs that may appear in equipment subjected to transient currents of hundreds or thousands of amperes. The final tasks of this CRADA included experiments and computer simulations used to investigate the effect of an arc breakdown between two planar electrodes and confined by various potting materials. The arc is generated by a current waveform with a rise of 8 μs and a 20 μs fall time. Voltage, current and light emissions are recorded. The developing shock wave is observed with an optical fiber embedded in the potting material and recorded using a photonic Doppler velocimetry (PDV) diagnostic system. The experiments were modeled using the ALEGRA-MHD simulation code and comparisons are made between the experiments and simulations. This product contains Protected CRADA Information which was produced December 2015 under CRADA No. 1651. l 5 and is not to be further disclosed for a period of 5 years from the date it was produced except as expressly provided for in the CRADA. Further dissemination only as authorized by the originating partners: the Boeing Company and Sandia National Laboratories Radiation Sciences Center, or higher U.S. Department of Energy programmatic authority. Please note that the project leads from both SNL and Boeing have all retired or left for other reasons at the time of the creation of this document. The final project manager collected information for this document from the final proposal and the final report.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning Thermodynamics And Kinetics of Defects For Accelerated Materials Discovery

Atomistic defects play a pivotal role in functional and structural materials’ performance across a myriad of technology applications. Quantitative prediction of the thermodynamics and kinetics of defect formation and migration, respectively, typically requires accurate but expensive first-principles approaches, such as density functional theory (DFT). Their computational expense limits the throughput needed to perform high-throughput materials discovery/screening exercises or to perform materials modeling tasks relying on extensive sampling techniques. Therefore, in this Sandia National Laboratories Laboratory Directed Research and Development (LDRD) project (Project #229366), we developed a variety of machine learning techniques, trained on density functional theory calculations, to accelerate the discovery and modeling of materials in which vacancy and interstitial defects primarily dictate material performance. These include applications such as metal oxides for water-splitting or mixed ionic-electronic conduction, metal hydrides for hydrogen storage, and transition metal dichalcogenides for electronics, and the approaches developed herein can further be applied to many other domains that similarly depend on materials’ thermodynamic and kinetic defect properties for their desired functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2015

New Insights into the Structure of Nanoporous Carbons from NMR, Raman, and Pair Distribution Function Analysis

The structural characterization of nanoporous carbons is a challenging task as they generally lack long-range order and can exhibit diverse local structures. Such characterization represents an important step toward understanding and improving the properties and functionality of porous carbons, yet few experimental techniques have been developed for this purpose. In this paper, we demonstrate the application of nuclear magnetic resonance (NMR) spectroscopy and pair distribution function (PDF) analysis as new tools to probe the local structures of porous carbons, alongside more conventional Raman spectroscopy. Together, the PDFs and the Raman spectra allow the local chemical bonding to be probed, with the bonding becoming more ordered for carbide-derived carbons (CDCs) synthesized at higher temperatures. The ring currents induced in the NMR experiment (and thus the observed NMR chemical shifts for adsorbed species) are strongly dependent on the size of the aromatic carbon domains. We exploit this property and use computer simulations to show that the carbon domain size increases with the temperature used in the carbon synthesis. The techniques developed here are applicable to a wide range of porous carbons and offer new insights into the structures of CDCs (conventional and vacuum-annealed) and coconut shell-derived activated carbons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Light-Induced Melting of Competing Stripe Orders without Introducing Superconductivity in La 2 − x Ba x CuO 4

The ultrafast manipulation of quantum material has led to many novel and significant discoveries. Among them, the light-induced transient superconductivity in cuprates achieved by melting competing stripe orders represents a highly appealing accomplishment. However, recent investigations have shown that the notion of photoinduced superconductivity remains a topic of controversy, and its elucidation solely through c -axis time-resolved terahertz spectroscopy remains an arduous task. Here, we measure the in-plane and out-of-plane transient terahertz responses simultaneously in the stripe-ordered nonsuperconducting La 2 − x Ba x CuO 4 after near-infrared excitations. We find that although a pump-induced reflectivity edge appears in the c -axis reflectance spectrum, the reflectivity along the CuO 2 planes decreases simultaneously, indicating an enhancement in the scattering rate of quasiparticles. This in-plane transient response is clearly distinct from the features associated with superconducting condensation. Therefore, we conclude the out-of-plane transient responses cannot be explained by an equivalent of Josephson tunneling. Notably, those pump-induced terahertz responses remain consistent even when we vary the near-infrared optical pump wavelengths and hole concentrations. Our results provide critical evidence that transient three-dimensional superconductivity cannot be induced by melting the competing stripe orders with pump pulses whose photon energy is much higher than the superconducting gap of cuprates. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗