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DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗

DOE OSTI2023

Ecologically-Informed Precision Conservation: A framework for increasing biodiversity in intensively managed agricultural landscapes with minimal sacrifice in crop production

Conservation actions are urgently needed to tackle biodiversity loss in intensively managed agricultural landscapes. Production lands are usually heterogeneous and contain low-yield areas that can be set aside for biodiversity conservation without serious yield losses. Here, we introduce Ecologically-Informed Precision Conservation, a framework that integrates yield mapping and ecological theory to select the best areas to create new set-asides while ensuring high crop yields at the farm/landscape level. Long-term yield maps can be generated using globally available satellite data and basic information on field/farm crop yield from farmers. Ecological principles are then used to select the subset of areas with the highest potential for biodiversity conservation by prioritising those that increase connectivity, maximise habitat heterogeneity and decrease landscape grain size. Here, the created non-crop habitats can be permanent and thus ensure biodiversity support over time. In addition, agricultural management efficiency can be enhanced by improving field shapes. The framework provides the basis for a practical, user-friendly tool that informs all interested stakeholders on how to rationalise existing agricultural landscapes using already-existing farming systems and available technologies. High cost-effectiveness from an economic and conservation perspective, along with the creation of heterogeneous non-crop habitats, make our framework a promising solution to re-design agricultural landscapes.

Precision agriculture↗

DOE OSTI2024

Generative $β$-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the -hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial -hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Unraveling design principles of protein landscapes in photosynthetic membranes in plant chloroplasts

The supramolecular organization of proteins within photosynthetic membranes is crucial for energy conversion in plants. Here, we introduce an analytical and computational pipeline that integrates high-resolution cryo–scanning electron microscopy, biochemical quantification, advanced Monte Carlo computer simulations, and statistical methods to elucidate the elusive protein landscapes of grana membranes in intact Arabidopsis leaves. Our integrated analysis challenges the prevailing view that particles on the exoplasmic fracture faces in freeze-fracture samples represent photosystem II exclusively. Instead, these particles also include cytochrome b 6 f complexes. Furthermore, our steric clash analysis demonstrates that stacked membranes contain a mixture of larger PSII supercomplexes (C 2 S 2 M 2 and C 2 S 2 ) in addition to a smaller complex (C 2 ). This suggests that in vivo PSII supercomplexes exist in an equilibrium distribution of differing sizes. Furthermore, we discovered that, although size exclusion effects govern the global protein arrangement, local packing exhibits orientational order indicative of lateral attractive protein-protein interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Artificial intelligence-driven approaches for materials design and discovery

Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial and error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence, have reshaped the landscape of designing new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. Here, in this Review, we present key computational advances in materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning methods and evolutionary algorithms, to advanced artificial intelligence strategies such as reinforcement learning and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This Review may serve as a brief guide to the approaches, progress and outlook of designing future functional materials with technological relevance.

computational methods↗

DOE OSTI2025

Available land for cellulosic biofuel production: a supply chain centered comparison

The land that is potentially available to produce dedicated cellulosic bioenergy crops, often referred to as 'marginal' land, depends heavily on the underlying assumptions used to classify and identify it. In this study we compare three definitions and types of marginal land to identify the interactions between the bioenergy landscape and the logistics networks needed for the biofuel supply chain. Typical studies of the scale, cost, and greenhouse gas (GHG) mitigation potential of cellulosic biofuel take a land-centered approach which may neglect to account for the trade-offs between establishing bioenergy crops and the supply chain design decisions needed to allow those crops to be converted to liquid fuel. A mathematical programming approach is used to minimize the total annualized cost of a large-scale field-to-product system producing bioethanol in the USA midwest. Results show that a high concentration of marginal land leads to efficient systems and that the bioenergy landscape design becomes increasingly important with a higher emphasis on GHG mitigation. Additionally, targeted landscape design (including fertilization) with a focus on fields with high soil carbon sequestration potential can greatly reduce the system-wide GHG emissions for only a small increase in the unit cost of biofuel.

09 BIOMASS FUELS↗

DOE OSTI2024

Challenges and Solutions for Leave-One-Out Biosensor Design in the Context of a Rugged Fitness Landscape

The leave-one-out (LOO) green fluorescent protein (GFP) approach to biosensor design combines computational protein design with split protein reconstitution. LOO-GFPs reversibly fold and gain fluorescence upon encountering the target peptide, which can be redefined by computational design of the LOO site. Such an approach can be used to create reusable biosensors for the early detection of emerging biological threats. Enlightening biophysical inferences for nine LOO-GFP biosensor libraries are presented, with target sequences from dengue, influenza, or HIV, replacing beta strands 7, 8, or 11. An initially low hit rate was traced to components of the energy function, manifesting in the over-rewarding of over-tight side chain packing. Also, screening by colony picking required a low library complexity, but designing a biosensor against a peptide of at least 12 residues requires a high-complexity library. This double-bind was solved using a “piecemeal” iterative design strategy. Also, designed LOO-GFPs fluoresced in the unbound state due to unwanted dimerization, but this was solved by fusing a fully functional prototype LOO-GFP to a fiber-forming protein, Drosophila ultrabithorax, creating a biosensor fiber. One influenza hemagglutinin biosensor is characterized here in detail, showing a shifted excitation/emission spectrum, a micromolar affinity for the target peptide, and an unexpected photo-switching ability.

Chemistry↗

NASA NTRS2004

Interplanetary navigation using a continental baseline large antenna arrays

Navigation is a key component of interplanetary missions and must continue to be precise with the changing landscape of antenna design. Improvements for the Deep Space Network (DSN) may include the use of antenna arrays to simulate the power of a larger single antenna at much lower operating and construction costs. Therefore, it is necessary to test the performance of arrayed antennas from a navigational point-of-view. This initial investigation focuses on the performance of arrayed antennas from a navigational point-of-view. This initial investigation focuses on the performance of delta one-way range measurements using a shorter baseline with more data collection then current systems use. With all other parameter equal, the longer the baseline, the better the accuracy for navigation making the number of data packets very important. This trade study compares baseline distances ranging from 1 to 1000km with an in use baseline, looking at a due east baseline, a due north baseline at 45 degrees East of North. The precision of the baseline systems can be found through a simulated created for this purpose using the Jet Propulsion Lab based Monte navigation and mission design tool. The simulation combines the delta one-way range measurements with two-range and two-way Doppler measurements and puts the measurements through a Kalman filter to determine an orbit solution. Noise is added along with initial errors to give the simulation realism. This study is an important step towards the assessment of the utility of arrays for navigational purposes. The preliminary results have showed a decrease in reliability as the baseline is shortened but the larger continental baselines show comparable results t that of the current Goldstone to Canberra.

very long baseline interferometry (VLBI)↗

DOE OSTI2025

Landscaper v1

Understanding the inner workings of machine learning models through their loss landscapes offers crucial insights into model properties, optimization dynamics, and generalizability. However, accessing these insights has traditionally required specialized mathematical expertise, limiting broader adoption. Landscaper is an open-source Python package designed to bridge this gap. Landscaper seamlessly integrates a suite of multi-dimensional loss landscape analyses with cutting-edge topological data analysis (TDA) methods. This powerful combination makes both fundamental loss landscape analysis and advanced TDA techniques accessible to the broader scientific ML community, without requiring deep pre-existing mathematical knowledge. Landscaper offers three key functionalities: * Construction: Builds detailed loss landscape representations through versatile low and high-dimensional sampling techniques. * Quantification: Applies advanced metrics, including a novel topological data analysis (TDA) based smoothness metric, enabling new perspectives on model behavior. * Visualization: Offers intuitive tools to visualize and interpret loss landscapes, providing actionable insights beyond traditional performance metrics.

Weber, Gunther [Lawrence Berkeley National Laborat↗

DOE OSTI2023

Land surface dynamics and meteorological forcings modulate land surface temperature characteristics

This study examines the effect of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics (LVCET) on land surface temperature (LST) distribution over West Africa (WA). We employ fourteen machine-learning models, which preserve nonlinear relationships, to downscale LST and other predictands while preserving the geographical variability of WA. Our results showed that the random forest model performs best in downscaling predictands. This is important for the sub-region since it has limited access to mainframes to power multiplex machine-learning algorithms. In contrast to the northern regions, the southern regions consistently exhibit healthy vegetation. Also, areas with unhealthy vegetation coincide with hot LST clusters. The positive Normalized Difference Vegetation Index (NDVI) trends in the Sahel underscore rainfall recovery and subsequent Sahelian greening. The southwesterly winds cause the upwelling of cold waters, lowering LST in southern WA and highlighting the cooling influence of water bodies on LST. Identifying regions with elevated LST is paramount for prioritizing greening initiatives, and our study underscores the importance of considering LVCET factors in urban planning. Topographic slope-facing angles, heat loads, and diurnal anisotropic heat all contribute to variations in LST, emphasizing the need for a holistic approach when designing resilient and sustainable landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Large-scale spatially explicit analysis of carbon capture at cellulosic biorefineries

The large-scale production of cellulosic biofuels would involve spatially distributed systems including biomass fields, logistics networks and biorefineries. Better understanding of the interactions between landscape-related decisions and the design of biorefineries with carbon capture and storage (CCS) in a supply chain context is needed to enable efficient systems. Here we analyse the cost and greenhouse gas mitigation potential for cellulosic biofuel supply chains in the US Midwest using realistic spatially explicit land availability and crop productivity data and consider fuel conversion technologies with detailed CCS design for their associated CO 2 streams. Optimization methods identify trade-offs and design strategies leading to systems with attractive environmental and economic performance. Strategic and operational decisions depend on underlying spatial features and are sensitive to biofuel demand and CCS incentives. US CCS incentives neglect to motivate greenhouse gas mitigation from all supply chain emission sources, which leverage spatial interactions between CCS, electricity prices and the biomass landscape.

09 BIOMASS FUELS↗

NASA NTRS2007

Broadband Achromatic Telecentric Lens

A new type of lens design features broadband achromatic performance as well as telecentricity, using a minimum number of spherical elements. With appropriate modifications, the lens design form can be tailored to cover the range of response of the focal-plane array, from Si (400-1,000 nm) to InGaAs (400-1,700 or 2,100 nm) or InSb/HgCdTe reaching to 2,500 nm. For reference, lenses typically are achromatized over the visible wavelength range of 480-650 nm. In remote sensing applications, there is a need for broadband achromatic telescopes, normally satisfied with mirror-based systems. However, mirror systems are not always feasible due to size or geometry restrictions. They also require expensive aspheric surfaces. Non-obscured mirror systems can be difficult to align and have a limited (essentially one-dimensional) field of view. Centrally obscured types have a two-dimensional but very limited field in addition to the obscuration. Telecentricity is a highly desirable property for matching typical spectrometer types, as well as for reducing the variation of the angle of incidence and cross-talk on the detector for simple camera types. This rotationally symmetric telescope with no obscuration and using spherical surfaces and selected glass types fills a need in the range of short focal lengths. It can be used as a compact front unit for a matched spectrometer, as an ultra-broadband camera objective lens, or as the optics of an integrated camera/spectrometer in which the wavelength information is obtained by the use of strip or linear variable filters on the focal plane array. This kind of camera and spectrometer system can find applications in remote sensing, as well as in-situ applications for geological mapping and characterization of minerals, ecological studies, and target detection and identification through spectral signatures. Commercially, the lens can be used in quality-control applications via spectral analysis. The lens design is based on the rear landscape lens with the aperture stop in front of all elements. This allows sufficient room for telecentricity in addition to making the stop easily accessible. The crucial design features are the use of a doublet with an ultra-low dispersion glass (fluorite or S-FPL53), and the use of a strong negative element, which enables flat field and telecentricity in conjunction with the last (field lens) element. The field lens also can be designed to be in contact with the array, a feature that is desirable in some applications. The lens has a 20deg field of view, for a 50-mm focal length, and is corrected over the range of wavelengths of 450-2,300 nm. Transverse color, which is the most pernicious aberration for spectroscopic work, is controlled at the level of 1 m or below at 0.7 m field and 5 m at full field. The maximum chief ray angle is less than 1.7 , providing good telecentricity. An additional feature of this lens is that it is made exclusively with glasses that provide good transmission up to 2,300 nm and even some transmission to 2,500 nm; thus, the lens can be used in applications that cover the entire solar-reflected spectrum. Alternative realizations are possible that provide enhanced resolution and even less transverse color over a narrower wavelength range.

Mouroulis, Pantazis↗

DOE OSTI2024

The Current Understanding of Mechanistic Pathways in Zeolite Crystallization

Zeolite catalysts and adsorbents have been an integral part of many commercial processes and are projected to play a significant role in emerging technologies to address the changing energy and environmental landscapes. The ability to rationally design zeolites with tailored properties relies on a fundamental understanding of crystallization pathways to strategically manipulate processes of nucleation and growth. The complexity of zeolite growth media engenders a diversity of crystallization mechanisms that can manifest at different synthesis stages. Here, in this review, we discuss the current understanding of classical and nonclassical pathways associated with the formation of (alumino)silicate zeolites. We begin with a brief overview of zeolite history and seminal advancements, followed by a comprehensive discussion of different classes of zeolite precursors with respect to their methods of assembly and physicochemical properties. The following two sections provide detailed discussions of nucleation and growth pathways wherein we emphasize general trends and highlight specific observations for select zeolite framework types. We then close with conclusions and future outlook to summarize key hypotheses, current knowledge gaps, and potential opportunities to guide zeolite synthesis toward a more exact science.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Analysis of carbon capture at cellulosic biorefineries

carbon capture and storage (CCS)↗

NASA NTRS1992

Optical pattern recognition III; Proceedings of the Meeting, Orlando, FL, Apr. 21, 22, 1992

Consideration is given to transitioning of optical processing into systems (TOPS), optical correlator hardware, phase-only optical correlation filters, optical distortion-invariant correlation filters, and optical neural networks. Particular attention is given to a test target for optical correlators, a TOPS electronic warfare channelizer program, a portable video-rate optical correlator, a joint transform correlator employing electron trapping materials, a novelty filtered optical correlator using a photorefractive crystal, a comparison of correlation performance of smart ternary phase-amplitude filters with gray-scale and binary input scenes, real-time distortion-tolerant composite filters for automatic target identification, landscaping the correlation surface, fast designing of a circular harmonic filter using simulated annealing, feature-based correlation filters for distortion invariance, automatic target recognition using a feature-based optical neural network, and a holographic inner-product processor for pattern recognition.

Casasent, David P.↗

DOE OSTI2024

Transforming Cyber Education thru Open to All Accessible Pathways

Boise State University’s (BSU) Cyber Operations and Resilience CORe program was intentionally designed so that any student, especially non-traditional and non-technical students, with an interest in cybersecurity could have an education and training pathway to enter the cyber workforce. The CORe curriculum focuses on teaching students how to design, apply, and improve cybersecurity through the interaction of people, processes, and technology. CORe is a stackable curriculum with elective credit hours and options for various academic and industry certificates and certifications that enable students to customize their unique career pathway. The CORe program guides students to think about the system being managed, the risks presented, and the dynamic intersection of system elements when considering how to incorporate resilience frameworks in achieving a resilient system. By developing systems thinking, the students gain an understanding of the interdependencies interacting with the operational system. Further, the CORe program encourages students to integrate cybersecurity knowledge with models and frameworks found in other academic disciplines through a unifying systems approach. CORe is designed around the realities of today’s broad cyber landscape: that breaches will occur in any system over time and proactive design of resilience into systems to detect, respond, and recover in a timely and orderly manner is critical. Students are taught to think holistically about cybersecurity focusing on all system elements. CORe is not a traditional cybersecurity degree. CORe is distinguished by the non-traditional engineering, computer science approach to cybersecurity education with the singular focus on infusing resilience operations and transdisciplinary systems thinking principles throughout the curriculum.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Application Guide for the Cyber-Resilient Design Framework for Hybrid Systems

As the energy landscape evolves, hybrid power plants—integrating multiple renewable energy sources (e.g., solar, wind, and battery storage) with the bulk power electric system—play a crucial role in meeting growing energy demands. However, with a hybrid power plant’s increased number of components, complex network connectivity and digitization of controls, these systems face growing cybersecurity risks. To help mitigate these risks, Idaho National Laboratory (INL) presents this application guide specifically designed for operators and systems engineers to evaluate the cybersecurity resilience of their hybrid power plant’s design.

14 SOLAR ENERGY↗