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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗
DOE OSTI2025

Comprehensive Database of Environmental Mitigations Extracted from FERC-Licensed Hydropower Projects Using Artificial Intelligence Techniques, 1998-2023

This dataset provides a comprehensive inventory of environmental mitigation measures required by Federal Energy Regulatory Commission (FERC) licensed hydropower facilities from 461 licenses that were issued from 1998 to 2023. These licenses constitute 446 of the 1015 FERC projects that were active at the end of 2023. 17,612 mentions of environmental mitigations were identified and categorized in 128 unique categories. Mitigations were identified using a Natural Language Processing (NLP) approach, specifically with a Bidirectional Encoder Representations from Transformer (BERT) model. Model-derived results were then reviewed and updated by a subject matter expert as needed. This dataset introduces important enhancements to previous efforts to inventory environmental mitigations, such as including associated license text for each mitigation, tracking the number of instances a mitigation was identified within a license, and providing improved location information. These enhancements significantly expand the dataset's utility, offering greater analytical capabilities and ensuring reproducibility. The dataset is downloadable as a zip file containing the metadata and dataset files.

Ruggles, Thomas [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

Covalent Organic Framework Photocatalysts for Water Oxidation and Overall Artificial Photosynthesis

Artificial photosynthesis through proton reduction or CO 2 reduction to generate chemical fuels has gained increasing attention as an attractive strategy for solar-to-fuel conversion. In these systems, the oxygen evolution reaction (OER) provides the necessary electrons and protons for driving the overall reaction but represents the rate-limiting step due to its inherently sluggish kinetics. Therefore, efficient overall artificial photosynthesis requires photocatalysts that can drive both the oxidation and reduction half-reactions, which impose stringent demands on catalyst design. Covalent organic frameworks (COFs) offer a versatile platform for designing such photocatalysts, owing to their strong light-harvesting capabilities , periodic architectures, and highly tunable frameworks that allow programmable catalytic sites and adjustable electronic band structures. While notable progress has been made in developing COF photocatalysts for the OER and overall artificial photosynthesis, these advances remain scattered across the literature and existing reviews, and a dedicated, systematic overview of the OER and its central role in integrated artificial photosynthetic processes is still lacking. This Review systematically summarizes recent advances in COF-based photocatalysts for the OER half-reaction and overall artificial photosynthesis. Our aim is to offer a comprehensive roadmap that establishes fundamental design principles for next-generation COF-based photocatalysts toward efficient and sustainable artificial photosynthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Effect of artificial viscosity on shocked particle-laden flows for staggered grid Lagrangian methods

Abstract Shocked particle-laden flows are important to many natural and industrial processes. When simulating these systems, artificial viscosity is often required to prevent numerical artifacts, such as ringing, from arising in the pressure and density fields. The linear and quadratic coefficients of the artificial viscosity determine the amount of smoothing that occurs in these fields. For particle-laden flows, however, many of the fluid–particle interaction forces, for example, the pressure gradient force and unsteady forces, depend on gradients in the fluid fields. Furthermore, while the shock passes over a particle, these forces can be more dominant than drag. This means that the artificial viscosity coefficients affect how a particle and fluid interact when simulating shocked particle systems. Here this effect is investigated for isolated particles and for a particle curtain using a staggered grid Lagrangian approach. The artificial viscosity coefficients have a significant impact on the maximum force that a fluid imparts to a particle, which is important for determining whether a particle will break up in response to the shock. Furthermore, it is found that the density ratio between the particle and the fluid is important in determining whether the artificial viscosity coefficients have a significant impact on the particle’s motion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Using Artificial Soiling to Rank Anti-Soiling Coatings for Arid Climates

A simple method to evaluate (e.g., screen and rank) the performance of coatings that are fully or partially anti-soiling (AS) is lacking within the PV industry. Artificial soiling may be used as a rapid and economical assessment approach, offering an efficient alternative to time-intensive, site-specific field testing. In this study, we present an artificial soiling method to replicate the anti-soiling performance rankings of two groups of coated glass samples supplied by two manufacturers (group C with CA, CP, and CS coatings; group A with AC coating). These are compared to field aging in two climates: semi-arid Lemoore, California over 4 months, and the hot desert in Mesa, Arizona over 7 months. Both field and indoor performance ranking utilize the transmittance ratio, defined as the optical transmittance between a coated sample and an uncoated reference in each group, as an evaluation metric to assess the effectiveness of the artificial soiling approach in replicating field soiling. It is critical to mimic the dominant field meteorological conditions associated with soiling-prone days and vulnerable times of day during the soiling season. Our results reveal that the use of artificial soiling for field performance ranking among coatings strongly depends on the soil type (composition and particle distribution), dust surface density, and prevalent environmental factors (wetting saturation by humidity, dew condensation extent, and prior weathering history of the coating). The similar rank order relative to the field suggests that the artificial soiling approach presented may be used for down-selecting anti-soiling coatings prior to prolonged field validation.

14 SOLAR ENERGY↗
DOE OSTI2026

Artificial Soiling Replication of Field Losses on Commercial Photovoltaic Modules

Here, this study demonstrates the capabilities of an indoor artificial soiling approach developed to closely replicate the natural, cyclic soil accumulation processes in the field-dust suspension, deposition, and sedimentation/cementation-for a subtropical climate. In this work, a near-field environment is replicated in an artificial soiling cubic chamber through controlled regulation of humidity, temperature, dust type, and dust concentration, based on site-specific historical climate data. Two different models (MA and MB) of full-size commercial photovoltaic modules from a single manufacturer, installed side by side in the mid-Atlantic United States, were retrieved and subjected to artificial soiling experiments and various characterization measurements, including short-circuit current, colorimetry, reflectance, X-ray fluorescence, laser diffraction, and optical microscopy. Both in the field and in our improved field-representative artificial soiling tests, the MA model experienced roughly twice the soiling loss as the MB model. To closely replicate the field soiling losses for a site-specific climate, it is critical to include: 1) The use of field-collected dust with identical dust chemistry and particle distribution instead of standardized ISO 12103 Arizona Road dusts, 2) the use of only a small amount of field-collected dust inside the chamber during the deposition process (e.g., 0.15 g), and 3) the preconditioning of the surface coating for the partial/full dose of UV stress as experienced in the field during sunlight exposure and the abrasion as experienced in the field during regular module cleaning activities, if/as needed. The field-representative artificial soiling method developed here could potentially be adopted for rank ordering of various antisoiling coatings developed by researchers and industry stakeholders.

14 SOLAR ENERGY↗
DOE OSTI2025

Performance Comparisons for Artificially Propagated and Wild Pacific Lamprey Juveniles and Larvae

ABSTRACT Artificially propagated Pacific lamprey ( Entosphenus tridentatus ) are produced for restoration and for use in dam passage studies to reduce the demand for wild fish. Such uses require that animals are representative of their wild counterparts. Previous work indicated that this is true for Pacific lamprey larvae and juveniles reared in the hatchery with respect to the length of sustained swimming. However, more subtle differences in behaviour and performance that lamprey need to survive have not been assessed. In this study, artificially propagated and wild fish were compared in laboratory tests under no‐flow conditions to examine light avoidance, burrowing speed, burst swim speed, volitional routine swim speed and time to come to rest. Most larvae burrowed in less than a minute, and we found highly significant differences ( p < 0.001) between artificially propagated and wild larvae burrowing times, a critical escape behaviour. This could have implications for studies of larval entrainment at irrigation diversion canals or in turbine boils at dams. Interestingly, all of the wild juveniles tested came to rest quickly after introduction to the chamber (1.5 min), while artificially propagated lamprey swam robotically near the surface and 48% did not come to rest in the first 10 min (median time to rest = 9.5 min). In contrast, wild juveniles quickly (median = 1.47 min) sought areas near the tank bottom and attached strongly with their oral disc. Such behavioural differences could have important survival consequences for artificially propagated lamprey as they approach turbine intakes, bypass screens and irrigation diversion headgates. This study highlights the need to conduct behavioural assays that examine subtleties of fish behaviour that can be missed with traditional swim tunnel comparisons.

Frick, Kinsey [Fish Ecology Division, Northwest Fi↗
DOE OSTI2025

Ice sculpting: An artificial spin ice Tutorial on controlling microstate and geometry for magnonics and neuromorphic computing

Artificial spin ice, arrays of strongly interacting nanomagnets, are complex magnetic systems with many emergent properties, rich microstate spaces, intrinsic physical memory, high-frequency dynamics in the GHz range, and compatibility with a broad range of measurement approaches. This Tutorial article aims to provide the foundational knowledge needed to understand, design, develop, and improve the dynamic properties of artificial spin ice. Special emphasis is placed on introducing the theory of micromagnetics, which describes the complex dynamics within these systems, along with their design, fabrication methods, and standard measurement and control techniques. The article begins with a review of the historical background, introducing the underlying physical phenomena and interactions that govern artificial spin ice. We then explore the standard experimental techniques used to prepare the microstate space of the nanomagnetic array and to characterize magnetization dynamics, both in artificial spin ice and more broadly in ferromagnetic materials. Finally, we introduce the basics of neuromorphic computing applied to the case of artificial spin ice systems with a goal to help researchers new to the field grasp these exciting new developments.

Sultana, Rawnak [Univ. of Delaware, Newark, DE (Un↗
DOE OSTI2025

An extension of the localized artificial diffusivity method for immiscible and high density ratio flows

The localized artificial diffusivity (LAD) method is widely regarded as the preferred multi-material regularization scheme for the compact finite difference method, because it is conservative, easy to implement, and generally robust for a wide range of multi-material problems. However, traditional LAD methods face significant challenges when applied to flows with large density ratios and when maintaining thermodynamic equilibrium across material interfaces. These limitations arise from the formulation of the artificial diffusivity flux and the reliance on enthalpy diffusion for interface regularization. Additionally, traditional LAD methods struggle to ensure stability under large density ratio conditions, fail to maintain a finite interface thickness, and are therefore unsuitable for modeling immiscible interfaces. Here, in this work, we discuss the origins of these issues in traditional LAD methods and propose modifications which enable the simulation of large density ratio and immiscible flows. The proposed method targets the artificial diffusion fluxes at gradients and ringing in the volume fraction, rather than the mass fraction in traditional methods, to consistently regularize large density ratio interfaces. Furthermore, the proposed method introduces an artificial bulk density diffusion term to enforce equilibrium conditions across interfaces. To address the challenge of modeling immiscible flows, a conservative diffuse interface term is incorporated into the formulation to ensure a finite interface thickness. Specific consideration is taken in the design of the method to ensure that these crucial properties are maintained for N -material flows. The effectiveness of the proposed method is demonstrated through a series of canonical test cases, and its accuracy is validated by comparison with experimental data on micro-bubble collapse in water. These results highlight the method’s robustness and its ability to overcome the limitations of traditional LAD approaches.

Artificial diffusivity↗
DOE OSTI2025

Automated Construction of Artificial Lattice Structures with Designer Electronic States

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Algorithms↗
DOE OSTI2025

Energy-relaxation pathways in finite-sized artificial square-ice structures

The energy-relaxation pathway in artificial spin ice structures refers to the series of individual moment reorientations the system undergoes as it relaxes from an initial state to a final low-energy state. The probability of the artificial spin ice structure reaching a ground state depends on the energy of the intermediate states as approximated by an Arrhenius-type relation. In this work, we report that by modifying the geometry of artificial square-ice structures, we can influence the probability of observing the ground state in different configurations. Furthermore, our findings are supported by dipolar energy calculations for various energy-relaxation pathways and the corresponding experimental data obtained by magnetic force microscopy.

Analog computation↗
DOE OSTI2024

Artificial Replication of Field Soiling Losses on PV Modules

In this paper, we experimentally demonstrate an improved replication of field soiling losses using an indoor artificial soiling chamber and tests on anti-soiling coated PV modules and coupons. The primary focus is to use site-specific soil collected from module surface and replicate the natural soiling processes including dust concentration in the air, slow and gradual dust accumulation and sedimentation on the module surface during the dominant soiling season of the site of interest. The experiments were conducted on two sample sets having different anti-soiling properties. The first set contains commercial modules with two different surface properties retrieved after three years of exposure from a single PV plant in a mid-Atlantic location; the other set contain glass coupons with three different coating materials that were installed and exposed over 4 months, at Lemoore, California. Major field-representative factors considered here for the close replication in the chamber include: the use of dust collected from modules surfaces at the outdoor sites to give the same dust chemistry; dust particle size distribution and concentration; the charge size of dust (< 0.15 g per injection); field humidity, and module temperature. The effectiveness of antisoiling coatings (or surface properties) for both sample sets were ranked in the artificial testing and were found to be closely matching with the field rank orders of the respective sites and sample sets. This paper provides the rank ordering results to objectively demonstrate the replication of field soiling losses in the artificial soiling chamber.

artificial soiling↗
DOE OSTI2026

Two Artificial Leaf Architectures for Solar Formate Production From CO 2 and H 2 O

Sunlight-powered artificial leaves for the production of formate from CO 2 are an attractive route to solar fuels, yet existing solar formate devices remain low in performance, and their architecture and material choices are underexplored. Herein, we report the fabrication of two distinct fully integrated, self-standing solar formate device architectures and elucidate the underlying design principles and material selection strategies. The first architecture integrates a Si photocathode with a BiVO 4 photoanode and utilizes a highly active Pd catalyst for CO 2 reduction. It represents the first artificial leaf device comprising two photoelectrodes (excluding photovoltaic [PV]-biased electrodes) for effective formate production under single-beam illumination. The second architecture employs a dark cathode and a dark anode driven by a 4-junction perovskite solar cell and uses a highly stable Bi catalyst for CO 2 reduction. This device delivers a record-high formate production rate of 174 µmol h −1 with a remarkable solar-to-formate energy efficiency of 2% among all artificial leaf devices reported to date. Finally, these results demonstrate the feasibility and outline the design principles of both PV-free and PV-assisted device architectures in solar fuel production.

electrocatalysis↗
DOE OSTI2025

Hydrologic response of artificially drained agricultural watersheds: insights from high-resolution integrated surface/subsurface simulations

Artificial drainage systems comprising subsurface networks of perforated pipes (tile drains) and engineered surface ditches are widely used to remove excess water from poorly drained agricultural regions. Artificial drainage lowers the water table by design but also has inadvertent effects on the watershed-scale hydrologic response with important implications for flood risk and nutrient exports. We investigated the effects of tile drains on watershed-scale hydrologic response in the Portage River, OH, Watershed using a high-resolution physically based integrated surface/subsurface hydrology model with recently developed capabilities to represent artificial drainage. Tile drains were found to enhance streamflow during times of low flow, generally consistent with previous studies. Streamflow flashiness was found to have a non-monotonic dependence on tile spacing with a minimum at intermediate spacings (∼50 m). Flashiness and the event hydrographs for small tile spacing were similar to the situation with no tiles, but flow paths from farm to stream were very different for those two end member cases, emphasizing the limitations of the stream hydrograph in characterizing hydrologic response. For typical tile spacings, peak flow can either be enhanced or attenuated by the presence of tiles, depending on the size of the event and the antecedent meteorological conditions. Tiles enhance peak flow when the event is below a threshold of ∼25 mm or when events arrive in dry conditions. Peak flow is reduced by tiles when events are large and arrive in conditions that are not overly dry. The dependence on event size and antecedent conditions is explained by differences in available storage and flow paths to the streams. These results provide additional insights into how tile drainage modulates event-scale hydrologic response, an important control on flood generation mechanisms and nutrient exports.

agricultural watershed↗
DOE OSTI2025

Topological Phenomena in Artificial Quantum Materials Revealed by Local Chern Markers

A striking example of frustration in physics is Hofstadter’s butterfly, a fractal structure that emerges from the competition between a crystal’s lattice periodicity and the magnetic length of an applied field. Current methods for predicting the topological invariants associated with Hofstadter’s butterfly are challenging or impossible to apply to a range of materials, including those that are disordered or lack a bulk spectral gap. Here, in this work, we demonstrate a framework for predicting a material’s local Chern markers using its position-space description and validate it against experimental observations of quantum transport in artificial graphene in a semiconductor heterostructure, inherently accounting for fabrication disorder strong enough to close the bulk spectral gap. By resolving local changes in the system’s topology, we reveal the topological origins of antidot-localized states that appear in artificial graphene in the presence of a magnetic field. Moreover, we show the breadth of this framework by simulating how Hofstadter’s butterfly emerges from an initially unpatterned 2D electron gas as the system’s potential strength is increased and predict that artificial graphene becomes a topological insulator at the critical magnetic field. Overall, we anticipate that a position-space approach to determine a material’s Chern invariant without requiring prior knowledge of its occupied states or bulk spectral gaps will enable a broad array of fundamental inquiries and provide a novel route to material discovery, especially in metallic, aperiodic, and disordered systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Calculating topological properties of artificial graphene in B-field

SAND2025-03258O Calculating topological properties of artificial graphene in B-field is a user-friendly tool designed to analyze artificial graphene systems influenced by magnetic fields. It helps researchers understand the unique properties of these materials by calculating the local Chern marker, a key indicator of their behavior. With just one file, this software runs easily on any device with Matlab, making it accessible for scientists and engineers. It provides valuable insights into the electronic characteristics of artificial graphene, which supports advancements in material science and technology, paving the way for innovative applications in electronics and beyond. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Spataru, Dan [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2024

Artificial Intelligence for Climate Change Mitigation Roadmap (Second Edition)

The first ICEF Artificial Intelligence for Climate Change Mitigation Roadmap was released in December 2023. Since that time, attention to artificial intelligence (AI) has continued to grow at a rapid pace. Tens of billions of dollars have poured into AI projects, policymakers around the world have considered new AI policies, and OpenAI reports that each month more than 200 million people now use ChatGPT. Signs of a changing climate continue to grow as well. Based on global average temperatures, July 22, 2024 was the warmest day ever recorded; 2023 was the warmest year ever recorded; and the 10 warmest years on record are the past 10 years. Yet global emissions of greenhouse gases continue to climb. Can AI help cut emissions of greenhouse gases? This Roadmap explores that question. In this second edition of the Artificial Intelligence for Climate Change Mitigation Roadmap, a team of 25 co-authors builds on last year’s roadmap—comprehensively updating all old chapters, adding six new chapters and offering 5–10 specific, actionable recommendations in each chapter. Our goal is to provide a useful resource for experts and non-experts alike. In Part I of this Roadmap, we provide brief introductions to both AI and climate change. In Part II, we explore eight sectors in which AI is helping respond to climate change and could do much more. In Part III, we explore crosscutting issues. We close with findings and recommendations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reconfigurable neuromorphic components and algorithms for next-generation artificial intelligence

Digital transistor-based general-purpose hardware (e.g., central processing units) is the dominant solution to support both traditional computing (logic, arithmetic, etc.) as well as modern artificial intelligence. State-of-the-art research has shown feasibility of post-digital physics-based neuromorphic hardware, which is hypothesized to support artificial intelligence algorithms with orders-of-magnitude improved time/energy efficiencies. But such research has not been widely deployed mainly because of such novel hardware’s extreme application-specificity, and the dominance of low-cost general-purpose (but inefficient) digital hardware. To make use of the novel algorithms and the superlative performance of physics-based hardware, we need to identify scientific principles that can enable generality in physics-based hardware. This work resulted in two important broad outcomes – first, we demonstrate fully reconfigurable neuromorphic components, and second, we demonstrate a viable artificial intelligence learning algorithm that can exploit the functioning of neuromorphic hardware. We demonstrate up to five orders of magnitude improvement in energy efficiency compared to the best general-purpose digital hardware.

97 MATHEMATICS AND COMPUTING↗