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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

2018 LDRD Annual Report (Argonne National Laboratory)

Argonne National Laboratory’s Laboratory Directed Research and Development (LDRD) program encourages the development of novel technical concepts, enhances the Laboratory’s research and development (R&D) capabilities, and enables pursuit of strategic laboratory goals. Argonne’s LDRD projects are proposal based and peer reviewed, supporting ideas that require advanced exploration so they can be sufficiently developed to pursue support through normal programmatic channels. Among the aims of the projects supported by the LDRD program are the establishment of engineering proofs of principle, assessment of design feasibility for prospective facilities, development of instrumentation or computational methods or systems, and discoveries in fundamental science and exploratory development. All LDRD projects have demonstrable ties to one or more of the science, energy, environment, and national security missions of the U.S. Department of Energy (DOE) and its National Nuclear Security Administration (NNSA), and many are also relevant to the missions of other federal agencies that sponsor work at Argonne. A natural consequence of the more “applied” type projects is their concurrent relevance to industry. The LDRD program is managed in overarching portfolios, each containing multiple projects each fiscal year. The LDRD Prime portfolio is further divided into strategic focus areas aligned with Argonne’s strategic plan. The largest component of Argonne’s program is LDRD Prime, which emphasizes R&D explicitly aligned with Laboratory major initiatives in support of Argonne’s strategic plan. The choice of Focus Areas under the LDRD Prime component reflects the major initiatives; the state of development of relevant technical fields; the potential value of advancing those fields to DOE/NNSA and the nation; and the compatibility of the fields with existing facilities, capabilities, and staff expertise at Argonne. Focus Areas with projects that ended in FY18 are: Advanced Computing, Biological and Environmental Science Capability Development, Energy Manufacturing Science and Engineering, Hard X-ray Sciences, Materials and Chemistry, Securing Energy and Critical Resources, and The Universe as Our Laboratory (ULab).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Studying light–matter interactions and energy transfer at the nanoscale with a trapped–ion quantum computer

A grand challenge in materials sciences is to control energy transduction, transfer and dissipation at the nanoscale. Meeting this challenge requires progress in nanoscale fabrication and synthesis, but equally importantly, also progress in our understanding of how nanoscale structure influences these phenomena. Although detailed quantum mechanical models exist that capture the structure and physics of materials and molecular species, solving these models to calculate the relevant emergent phenomena, which often occur over vastly different time and energy scales, has so far proven difficult. In this project, we studied how to model energy transfer and light-matter interactions using trapped-ion quantum computers. Our studies focused on emulation of critical processes in natural systems, such as the well-known photosynthetic pigment-protein complexes responsible for capturing and transducing light to electrons. In addition, we implemented proof-of-principle experiments demonstrating that such calculations are possible on trapped-ion quantum computing hardware. These studies and experiments pave the way to investigate energy transport and transduction in a detail not accessible with classical computing technologies. We expect that our studies will allow to extract important guiding principles on how to improve the efficiency of nano-scale light-harvesting and transduction devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Controlling Catalyst–Semiconductor Contacts: Interfacial Charge Separation in p-InP Photocathodes

Charge-carrier-selective interfaces between electrocatalyst particles and semiconductor light absorbers are critical for solar photochemistry but controlling their properties is challenging. Using thin films and nanoparticle arrays of Pt hydrogen-evolution catalysts on p-InP (a high-performance photocathode material), along with macroscopic and nanoscopic electrical and chemical analysis, we show how hydrogen alloying, the pinch-off effect for nanoscale contacts, and the formation of a native surface oxides all play different roles in creating charge-carrier-selective junctions. As a result, the new insights can be broadly applied to photocathodes, photoanodes, and overall water-splitting systems to control charge-carrier selectivity and improve performance.

14 SOLAR ENERGY↗
DOE OSTI2024

Developing and Evaluating Energy Justice Metrics for Early-Stage Materials Research

Materials science is a central component of early-stage research and development of virtually all clean energy technologies. But as much as material breakthroughs often hold the key to high efficiencies, long lifetimes, and high stability in eventual devices, early-stage choices about material types, structures, and processing can also serve to lock in long-term social and equity impacts of deployed energy technologies. Thus, to achieve a just and sustainable energy transition, tools to assess the energy justice impacts of early-stage materials research are critical. Here, we discuss development of the Justice Underpinning Science and Technology Research (JUST-R) metrics framework - a suite of metrics targeted at early-stage researchers to assess energy justice considerations in their work. The framework is evaluated for its appeal to researchers and effectiveness at promoting integration of energy justice into research through case studies, which reveal its ability to broaden researcher perspectives and key avenues for future improvement.

energy justice↗
DOE OSTI2023

ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites

Zeolites are one of the most widely used materials in the chemical industry due to their nanometer-sized pores that can adsorb and react upon molecules selectively. With hundreds of known framework topologies and hundreds of thousands of computationally predicted structures, the ability to rapidly predict zeolite performance allows researchers to prioritize their efforts on the most promising structures for a given application. Although the accuracy of forcefield-based atomistic simulations has advanced significantly in the past two decades, these simulations can be computationally expensive, especially for long-chain, complex molecules. Here, we present ZeoNet, a representation learning framework using convolutional neural networks (ConvNets) and 3D volumetric representations for predicting adsorption in zeolites. ZeoNet was trained on the task of predicting Henry's constants for adsorption, k H , of n-octadecane in more than 330 000 known and predicted zeolite materials. Employing a 3D grid based on the distances to solvent-accessible surfaces, a volumetric representation that can be generated efficiently, the best-performing ZeoNet achieved a correlation coefficient r 2 = 0.977 and a mean-squared error MSE = 3.8 in ln k H , which corresponds to an error of 9.3 kJ mol -1 in adsorption free energy. In comparison, a model based on hand-designed geometric features has values of r 2 = 0.783 and MSE = 35.7. ZeoNet is also relatively efficient and can process ≈8 structures per second on an Nvidia RTX 2080TI GPU, orders of magnitude faster than forcefield-based simulations. A systematic analysis was conducted to investigate how the choice of ConvNet architectures, the linear dimension (L) and spatial resolution (Δd) of the distance grids, batch size, optimizer, and learning rate impact the model performance. We found that ConvNets based on the ResNet architecture offer the best tradeoff between expressiveness and efficiency. The performance for all models reaches a plateau at L = 30–45 Å and depends less sensitively on grid resolution, with a small benefit around Δd = 0.30–0.45 Å. Finally, saliency maps were visualized to identify which regions of the materials contributed the most to model predictions. It was found, interestingly, that the predictions are driven primarily by the accessible pore volume rather than the region occupied by the framework atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

How additives for tin halide perovskites influence the Sn 4+ concentration

Tin halide perovskite (Sn-HPs) photovoltaics could potentially equal or exceed the performance of their more toxic Pb-based analogues if defect state densities, particularly originating from the presence of Sn 4+ , can be significantly decreased. Numerous additives are incorporated into Sn-HPs to minimize the amount of Sn 4+ , including SnF 2 , reducing agents such as hydrazine derivatives, and various antioxidants. However, despite the frequent use of additives to reduce Sn 4+ content, there is limited understanding of how they function and consequently limited guidance for the development of new additives. Herein, we use cyclic voltammetry to probe the redox behavior of SnI 2 , SnI 4 , Sn-HP precursor solutions, and 18 different additives. Through 119 Sn NMR measurements we show that hydrochloride containing additives undergo halide exchange with SnI 4 to form SnI x Cl y , which results in decreased Sn 4+ concentrations and less p-type character in the Sn-HP films. We find that the most effective additive at lowering the Sn 4+ content in FASnI 3 is not capable of reducing SnI 4 or forming SnI x Cl y , but rather it acts as a sacrificial and coordinating antioxidant. In general, when selecting additives for Sn-HPs it is important to account for the redox potential, coordination with Sn species, ability to react with oxygen, and the potential for halide exchange.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Moving closer to experimental level materials property prediction using AI

Abstract While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$ > 0.076 eV/atom) for the first time.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High throughput structure–property relationship for additively manufactured 316L/IN625 alloy mixtures leveraging 2-step Bayesian estimation

While the fabrication of graded materials by directed energy deposition (DED) has led to accelerated materials discovery, the ability to rapidly explore sufficiently large material composition spaces is limited due to the time-intensive nature of conventional materials characterization techniques. The present study investigates the viability of small punch test (SPT) protocols for rapidly evaluating DED-fabricated alloy mixtures of stainless steel 316L (316L) and Inconel 625 (IN625). The SPT protocols evaluated in this study include both the recently established two-step Bayesian estimation framework as well as the empirical relationships established in prior literature. It is shown that these protocols are capable of reliably and quantitatively tracking the changes in the mechanical properties of the alloy mixtures studied. Enhancement of mechanical properties was observed with the addition of IN625 to 316L, which is attributed to the austenite stabilization in the matrix and the formation of fine δ - Ni3Nb precipitates. It is shown that CALPHAD-based Scheil model simulations predicted the formation of different precipitate phases for each composition. The novel protocols presented in this paper open new avenues for high throughput material explorations for additive manufacturing.

2-step Bayesian estimation↗
DOE OSTI2021

Thermally Stable Single-Atom Heterogeneous Catalysts

Single-atom catalysts (SACs) have attracted extensive attention in fields related to energy, environment, and material sciences because of the high atom efficiency and the unique properties of these materials. Many approaches have hitherto been successfully established to prepare SACs, including impregnation, pyrolysis-involved processes, atom trapping, and coprecipitation. However, under typical reaction conditions, single atoms on catalysts tend to migrate or agglomerate, forming nanoclusters or nanoparticles, which lowers their surface free energy. Efforts are required to develop strategies for improving the thermal stability of SACs while achieving excellent catalytic performance. Here in this Progress Report, recent advances in the development of thermally durable single-atom heterogeneous catalysts are discussed. Several important preparation approaches for thermally stable SACs are described in this article. Fundamental understanding of the coordination structures of thermally stable single atom prepared by these methods is discussed. Furthermore, the catalytic performances of these thermally stable SACs are reviewed, including their activity and stability. Finally, a perspective of this important and rapidly evolving research field is provided.

coordination structure↗
DOE OSTI2024

Submerged Fatigue Testing of Marine Energy Advanced Materials

Marine energy structures are typically made of advanced composite materials and are subjected to extreme ocean environments in service. In extreme ocean environments, seawater currents and waves load structures repeatedly, which cause two environmental conditions: water intrusion and mechanical fatigue. In prior research, the two environmental conditions were applied sequentially, where composite specimens were aged and then mechanically tested. To understand the combined effects of dynamic loading and water intrusion on composite materials, the present study involves the static and fatigue four-point bend testing of composite coupons in a water tank. The water tank was designed and built to fit either a 100 kN or a 250 kN load frame. Flexural strength value, cycles to failure, and failure mode results from submerged fatigue testing will be used to inform marine energy structure designs. The coupon-scale test method will be used to scale up to and inform methods for subsequent subcomponent testing and standards development. Finally, the benefits of designing marine energy structures to informed standards are decreased lifetime costs and increased reliability and energy production, ultimately leading to a sustainable and low-carbon energy system.

13 HYDRO ENERGY↗
DOE OSTI2022

Topological Materials for Functional Optoelectronic Devices

The recent realization of topology as a mathematical concept in condensed matter systems has shattered Landau's widely accepted classification of phases by spontaneous symmetry breaking as he famously said, “a particular symmetry property exists or does not exist.” Topological materials (TMs) such as topological insulators and topological semimetals, are characterized by properties that depend on the topology of the band structure. Such dependence has drastic implications on the optical, electrical, and thermal properties of the material. Fundamental physics of TMs is currently under active research in condensed matter, materials science, and high energy physics. Here, recent advances in exploiting the unique properties of TMs to realize functional optoelectronic devices are surveyed. Current and future applications that are, or may be, enabled by their unique properties are discussed. Although many theoretical ideas have been proposed over the past decade or so on using TMs in optoelectronic applications, the focus will be on experimentally realized devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Computational Approaches for Clean Energy Materials

Currently, 80% of the global final energy consumption occurs in form of fuels and only 20% as electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, a successful energy transition will require enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies. As much as photovoltaic capacities have grown over the past 20 years, it is far from clear that current technologies and materials are up to the task to grow from here by yet another factor 100 until 2050. Therefore, sustained research efforts on emerging inorganic semiconductors for solar electricity and fuels are essential for facing the double challenge of climate change and energy security. Computational materials science can make important contributions, guiding and supporting research activities through both materials search and discovery and through detailed studies that help to develop a mechanistic understanding of materials performance and bottlenecks. This presentation will highlight three recent computational projects with relevance for photovoltaics and solar fuels (1) Defect graph neural networks (dGNN) for materials discovery in solar thermochemical hydrogen (STCH) [1]. The dGNN approach facilitates broad and fast materials screening for defect properties. (2) Modeling highly off-stoichiometric systems by evaluating the free energy of defect interaction [2]. This approach allows quantitative prediction of H2 production in complex STCH oxides. (3) First-principles atomic structure prediction for interfaces [3]. This work showed how an atomically thin CdCl2 interlayer phase enables in principle ideal electron transport across the incommensurate SnO2/CdTe interface. [1] M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. (2023). https://doi.org/10.1038/s43588-023-00495-2. [2] A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024). https://doi.org/10.1103/PRXEnergy.3.013008. [3] A. Sharan, M. Nardone, D. Krasikov, N. Singh, S. Lany, Appl. Phys. Rev. 9, 041411 (2022). https://doi.org/10.1063/5.0104008.

density functional theory↗
DOE OSTI2026

Visualization for Insight and Data Analysis in Energy Research

This talk explores how advanced visualization technologies are transforming analytical reasoning and knowledge discovery in energy research, drawing on recent work at the National Laboratory of the Rockies' Computational Science Center. Through a series of scientific case studies, we demonstrate how immersive and high-resolution visualization environments enable scientists and engineers to identify previously unseen patterns and features - insights that often remain hidden in traditional desktop-based analysis. By embedding richer information into interactive analytics tools, these approaches support the exploration of complex, multivariate parameter spaces, where interaction itself catalyzes understanding. Beyond capability, we emphasize the critical role of visualization design grounded in perception and cognition, showing how visual encodings directly influence analytical outcomes. Spanning applications from materials science to integrated energy systems, these visualization approaches accelerate innovation and improve decision-making by enabling deeper, more reliable insight into increasingly complex energy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Spatiotemporal Reaction Dynamics Control in Two‐Photon Polymerization for Enhancing Writing Characteristics

Since 2001, 3D microfabrication based on two‐photon polymerization (TPP) has drawn extensive attention and interest in biology, optics, photonics, material science, and high‐energy physics. The in‐volume fabrication capability due to the threshold behavior of two‐photon absorption enables TPP higher flexibility compared with other nanofabrication techniques. However, as determined by the in‐volume fabrication feature as well as various reaction dynamics, the writing characteristics of TPP, such as throughput, accuracy, surface quality, and fabrication capability, are still limited. Herein, a comprehensive study is performed on the spatiotemporal behavior of reaction dynamics during TPP fabrication, mainly focusing on spatiotemporal characteristics of radical diffusion, photothermal effect, microscale mechanics, and voxel stacking process. Based on the study, a nonsequential fabrication method is established to simultaneously improve key writing characteristics of TPP and realize sharp features, high speeds, large overhang structure, and smooth surfaces. The method established in this work can be applied to improve the performance of functional devices for various fields.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fusion Materials Research at Oak Ridge National Laboratory in Fiscal Year 2023

The materials science challenge of providing a suite of suitable materials to satisfy the technology to achieve fusion energy is addressed in this ORNL program. The inability of currently available materials and components to withstand the harsh fusion nuclear environment requires development of new materials, and an understanding of their response to the fusion environment. The overarching goal of the ORNL Fusion Materials program is to provide the applied materials science support and materials understanding to underpin the ongoing DOE Office of Science—Fusion Energy Sciences program, in parallel with developing the materials for fusion power systems. In this effort the program continues to be integrated both with the larger U.S. and international fusion materials communities and with the U.S. and international fusion design and technology communities. The excitement of this program comes from the priorities given to this subject in the two recent fusion reviews, by the FESAC and NAS committees. An important element of those recommendations is the support for pivoting the national R&D emphasis to the Fusion Materials and Technologies (FM&T), the long-advocated Fusion Prototypic Neutron Source, and for the Fusion Pilot Plant study that will help focus program direction and efforts. Furthermore, the surge of venture capital investment into the private fusion industry start-ups over the last few years is anticipated to help accelerate all aspects of the fusion energy development. This twelfth annual report of the ORNL (Oak Ridge National Laboratory) Fusion Reactor Materials Program summarizes the accomplishments in Fiscal Year 2023 (FY2023). The year was the first to return to full post-COVID-restriction operations, with students and international assignees no longer impacted by COVID restrictions, as in FY20-21-22. Following the pattern of planning used in this program, work for the year FY2023 focused on having the data and productivity to support a strong presence at the International Conference on Fusion Reactor Materials (ICFRM) 21, organized by Spain and occurred in October 2023. Twenty-nine ORNL-led abstracts were submitted, with all accepted. Four were invited presentations, nine contributed oral, fourteen posters, and two withdrawn due to unforeseen circumstances. Additionally, nine external abstracts with ORNL contributing authors were presented. These will be reported in the FY24 report next year.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗