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DOE OSTI2026

Direct Ab Initio Simulation of the Synthesis of BaZrO 3 and the Microstructure Impacts on Proton Transport

Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performance impact of microstructures formed under synthesis conditions is required to develop advanced materials, such as solid-state fuel cells and electrolyzers. Using the ceramic BaZrO 3 as a case study, we directly simulate the synthesis and investigate how proton transport is dictated by microstructures. We develop a framework that couples density functional theory (DFT), machine-learning interatomic potential (MLIP) driven molecular dynamics, and grand canonical Monte Carlo to perform large-scale, microstructure-resolved, atomistic simulations of proton transport in experimentally representative polycrystalline structures. Our fully ab initio approach, using a MLIP as a proxy for DFT, allows us to quantify the competition between two distinct diffusion mechanisms: one associated with grain-boundary regions and another within grains. When the impacts of grain boundaries are taken into account, proton transport exhibits substantial deviation from the bulk oxide limit. This addresses long-standing discrepancies between theory and experiments. Our integrated approach provides atomistic insight into microstructure-dependent proton pathways in BaZrO 3 and establishes a general protocol for predicting processing-structure-performance relationships.

organic↗

DOE OSTI2025

Molecular Design Considerations for Azobenzene Anolytes

Realization of high-density batteries requires the development of anolytes that display highly negative reduction potentials, solubility, and persistence in the charged state. Azobenzenes have garnered interest as potential anolytes for redox flow batteries. Here, we report the synthesis of a family of substituted azobenzene derivatives and evaluation of their solution-phase electrochemical properties. Systematic synthetic derivatization of this scaffold allows (1) access to anolytes of varying solubility, including intrinsically liquid derivatives that represent potential high-density charge carriers; (2) systematic variation of the reduction potential, and in some cases redox inventory, that provides azobenzenes with highly negative reduction potentials; and (3) control of the lifetime of the azobenzene radical anions that result from one-electron reduction. Electrokinetic experiments demonstrated that fast electron transfer occurs for all derivatives examined. Spectroscopic characterization of monoreduced azobenzene derivatives establishes that decomposition of the azobenzene radical anion proceeds via bimolecular disproportionation. Together, these results provide an experimental basis for the optimization of azobenzene anolytes for electrochemical storage applications, including redox flow batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Why Seeding Works When Nucleation Barriers Vanish

Crystallization is a process governed by the interplay between nucleation and growth. While crystalline seeds are known to reduce nucleation barriers and accelerate crystallization under nucleation-limited conditions, their influence when nucleation is not the limiting step remains poorly understood. This creates a mechanistic puzzle in systems where nucleation barriers are already negligible, yet seeding still accelerates crystallization. Here, the synthesis of zeolites is a quintessential example of growth-limited crystallization in which seeds accelerate the process despite negligible homogeneous nucleation barriers. Using coarse-grained molecular dynamics simulations─validated across two zeolites and the unrelated case of ice crystallization─we establish that growth-limited crystallization produces many small, misoriented crystallites whose slow coarsening into larger domains controls the emergence of X-ray-detectable crystallinity. Local crystalline order, structural coherence, and X-ray detectability are therefore kinetically decoupled milestones: the first can be reached rapidly while the latter two lag significantly. Seeds resolve this lag by imposing a common orientational registry on nascent crystallites, enabling their coherent coalescence into large seed-bound domains. This early coherence-building step produces a crystallite-size asymmetry that accelerates subsequent coarsening, advancing the onset of X-ray-detectable crystallinity without necessarily increasing the nucleated fraction. We conclude that under growth-limited conditions, the apparent induction period observed in powder X-ray diffraction reflects the time required to build long-range coherence, not the time to form crystalline material. Accordingly, seeds function not primarily by reducing nucleation barriers but by enforcing spatial coherence, thereby shortening the time required to develop long-range order detectable by X-ray diffraction.

Growth-Limited Crystallization↗

DOE OSTI2025

Rising infrastructure inequalities accompany urbanization and economic development

Impending global urban population growth is expected to occur with considerable infrastructure expansion. However, our understanding of attendant infrastructure inequalities is limited, highlighting a critical knowledge gap in the sustainable development implications of urbanization. Using satellite data from 2000 to 2019, we examine country-level population-adjusted biases in infrastructure distribution within and between regions of varying urbanization levels and derive four key findings. First, we find long-run positive associations between infrastructure inequalities and both urbanization and economic development. Second, our estimates highlight increasing infrastructure inequalities across most of the countries examined. Third, we find greater future infrastructure inequality increases in the global south, where inequalities will rise more in countries with substantial urban primacy. Fourth, we find that infrastructure inequality may evolve differently than economic inequalities. Overall, advancing sustainable development vis-à-vis urbanization and economic development will require intentional infrastructure planning for spatial equity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Polyketide synthase-based controlled synthesis of polycyclopropanated fuel molecules

Reducing carbon emissions from aviation and long-distance transportation sectors requires the development of sustainable biofuels with suitable energy density, freezing point, and other physical properties. We previously demonstrated biological production of high energy polycyclopropanated fatty acids (POP-FAs, class I) using an iterative polyketide synthase (iPKS) pathway in a Streptomyces host. Here, we used a computational model of fuel properties to identify chain length and cyclopropanation control as critical steps to engineer this iPKS for biofuel applications. We next explored the natural diversity of POP biosynthesis by investigating homologous pathways. Then, by in vivo gene exchange, we determined cyclopropanase (CP) catalysis to be key for POP-FA engineering. Leveraging both natural and engineered pathway product diversity, we demonstrate targeted production of improved POP-FAs, namely shortened POP-FAs with predicted superior freezing point properties for aviation, as well as fully cyclopropane-saturated POP-FAs which should have superior energy-density. These precise and controllable modifications to POP-FA structure open the door for bioproduction of designer POP fuels.

Yin, Kevin↗

DOE OSTI2025

Symmetry-protected electronic metastability in an optically driven cuprate ladder

Optically excited quantum materials exhibit non-equilibrium states with remarkable emergent properties, but these phenomena are usually transient, decaying on picosecond timescales and limiting practical applications. Advancing the design and control of non-equilibrium phases requires the development of targeted strategies to achieve long-lived, metastable phases. Here, in this study, we report the discovery of symmetry-protected electronic metastability in the model cuprate ladder Sr 14 Cu 24 O 41 . Using femtosecond resonant X-ray scattering and spectroscopy, we show that this metastability is driven by a transfer of holes from chain-like charge reservoirs into the ladders. This ultrafast charge redistribution arises from the optical dressing and activation of a hopping pathway that is forbidden by symmetry at equilibrium. Relaxation back to the ground state is, hence, suppressed after the pump coherence dissipates. Our findings highlight how dressing materials with electromagnetic fields can dynamically activate terms in the electronic Hamiltonian, and provide a rational design strategy for non-equilibrium phases of matter.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Efficient and assured reinforcement learning-based building HVAC control with heterogeneous expert-guided training

Abstract Building heating, ventilation, and air conditioning (HVAC) systems account for nearly half of building energy consumption and $$20\%$$ of total energy consumption in the US. Their operation is also crucial for ensuring the physical and mental health of building occupants. Compared with traditional model-based HVAC control methods, the recent model-free deep reinforcement learning (DRL) based methods have shown good performance while do not require the development of detailed and costly physical models. However, these model-free DRL approaches often suffer from long training time to reach a good performance, which is a major obstacle for their practical deployment. In this work, we present a systematic approach to accelerate online reinforcement learning for HVAC control by taking full advantage of the knowledge from domain experts in various forms . Specifically, the algorithm stages include learning expert functions from existing abstract physical models and from historical data via offline reinforcement learning, integrating the expert functions with rule-based guidelines, conducting training guided by the integrated expert function and performing policy initialization from distilled expert function. Moreover, to ensure that the learned DRL-based HVAC controller can effectively keep room temperature within the comfortable range for occupants, we design a runtime shielding framework to reduce the temperature violation rate and incorporate the learned controller into it. Experimental results demonstrate up to 8.8 X speedup in DRL training from our approach over previous methods, with low temperature violation rate.

Xu, Shichao↗

DOE OSTI2025

Colloidal quantum dots for optoelectronics

Colloidal quantum dots (QDs) are semiconductor nanocrystals that have unique size-tunable optoelectronic properties and are suitable for wet processing. QD research aims to answer fundamental questions about the chemical and physical properties of nanoscale materials and use these tools for technological applications ranging from bio-imaging to quantum optics. At the core of this field is a set of synthetic, processing and analytical methods designed to produce QDs in uniform ensembles that meet the highest performance standards. Here, this Primer reviews QD fabrication methods with a focus on the applications of QDs in printed optoelectronics and quantum optics. After outlining the current state-of-the-art QD syntheses, the experimental and computational analysis of QDs is discussed. These topics are then connected to the methodologies, processes and concepts required for developing QD-based photodetectors, light-emitting devices and quantum optics applications. Special attention is paid to challenges in reproducibility and current limitations of the field, such as the need to balance non-restricted material composition with high performing technology while achieving long-term stability in QD devices under operating conditions. Finally, the ongoing advancement in QD synthesis, precise atomic-level analysis and computational methodologies are highlighted as key drivers towards rational QD design, particularly in understanding how structural changes under loading impact QD properties.

optical materials↗

DOE OSTI2026

A mathematical framework for thermodynamic computing with applications to chemical reaction networks

The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.

Cannon, William R. [Pacific Northwest National Lab↗

DOE OSTI2025

Scientific frontiers of agrivoltaic cropping systems

Agrivoltaic (AV) systems integrate agriculture with electricity conversion through photovoltaic (PV) modules. Compared with conventional ground-mounted PV systems, AV systems can reduce land-use competition and offer agronomic and economic advantages, such as more stable crop production and additional farm income. However, AV systems can decrease agricultural performance and are typically 20-90% costlier to install than conventional PV systems. Here, in this Review, we analyse the implementation of AV cropping systems to preserve agricultural activities and highlight challenges and barriers. The global electricity potential of AV systems is ~66-385 PWh annually, depending on PV technology and installation density, if deployed in the most suitable areas, without accounting for grid availability. Scaling up has been hindered by crop selection for shading conditions, decreased energy conversion per unit of land area and issues with social acceptance, landscape impact and environmental sustainability. These issues can be addressed by developments such as wavelength-selective PV; system configurations, such as optimizing module spacing to reduce shading; and operational methods, such as optimizing tracking strategies and integrating agricultural infrastructure. Cross-sector policies can support AV systems by addressing the needs of diverse stakeholders over shared land resources. Further development will require collaboration among the design, performance, deployment and systems research communities.

14 SOLAR ENERGY↗

DOE OSTI2025

Advances in ionic liquid recycling for lignocellulosic biomass pretreatment

Ionic liquids (ILs) are promising solvents for the pretreatment of lignocellulosic biomass due to their ability to disrupt cellulose, hemicellulose, and lignin structures. However, large-scale implementation requires the development of efficient recovery and recycling methods. This review provides a comprehensive analysis of the recyclability potential of ILs used in biomass pretreatment, emphasizing their mechanisms, recent innovations, and ongoing challenges. We begin by discussing the structural diversity and tunability of ILs, which underlie their effectiveness in biomass deconstruction. The distinct roles of IL anions and cations in dissolving specific biomass components are systematically presented and compared. Advances in IL recycling techniques, including antisolvent precipitation methods, membrane separation, and distillation, are critically examined, with attention to how mechanistic insights can inform the design of more efficient and selective recovery strategies. Despite progress, significant challenges remain to scaling up IL-based biomass processing, including high cost, environmental concerns, and impact of biomass-derived impurities (e.g., lignin residues, sugars, proteins) on IL purity and functionality after reuse. We also review the applicability of different ILs based on life cycle assessments and techno-economic analyses. Lastly, we identify critical research gaps and propose future directions, including the design and development of next-generation ILs with improved recyclability, reduced toxicity, and enhanced economic viability for industrial-scale applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Shock propagation in aerogel and TPP foams for inertial fusion energy target design

Achieving practical inertial fusion energy (IFE) requires the development of target designs with well-characterized microstructure and compression response. We measured shock dynamics in low-density (17.5–500 mg/cm 3 ) aerogel and two-photon polymerization (TPP) foams using x-ray phase contrast imaging (XPCI) methods and the Velocity Interferometer System for Any Reflector. By analyzing shock front evolution, we examined how target type and density influence shock propagation and energy dissipation. Talbot-XPCI shows that aerogels support a smooth, bowed shock front due to their homogeneous nanometer-scale pore network. In contrast, TPP foams exhibit irregular, stepwise propagation driven by interactions with their periodic micrometer-scale lattice. Shock velocity follows a power-law relation: aerogels deviate from classical ρ −1/2 scaling due to pore-collapse dissipation, while TPP foams follow the trend with larger uncertainties from density variations. Comparisons with xRAGE simulations reveal systematic underestimation of shock speeds. These results provide the first experimental constraints on shock propagation in TPP foams over a wide density range and highlight the influence of internal structure on anisotropic shock behavior. Our findings support improved benchmarking of EOS and hydrodynamic models and inform the design of foam architectures that promote implosion symmetry in IFE capsules.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗

DOE OSTI2025

Where to cool off: a geospatial framework for placement of cooling centers

Indoor cooling is essential to reduce heat stress and increase passive survivability during heatwaves. Although air conditioning (AC) is recommended for maintaining indoor thermal comfort, low- and medium-income households in the U.S. often do not own an AC and/or limit AC usage to reduce energy consumption and associated costs, thereby risking their health and safety. With the frequency and intensity of heatwaves increasing, cooling centers are considered an appropriate alternative to indoor cooling and a possible mitigation strategy to prevent adverse health impacts of heat exposure. However, these centers are limited in numbers and not always accessible. This requires (i) developing a geospatial framework using physical and social factors for optimal siting of cooling centers to meet future needs and (ii) ranking of existing and potential cooling centers (schools, libraries, religious institutions) based on their accessibility among vulnerable populations and proximity to healthcare facilities. We developed and deployed a geospatial framework based on the Multi-criteria Decision Analysis approach in five U.S. cities (Los Angeles (LA), Phoenix, Austin, Atlanta, Miami) to evaluate the effectiveness of the framework in ranking cooling centers based on accessibility and population coverage. The results revealed that (i) access to cooling centers varies across cities and 32.2–50.7% of centers are within walking distance of the most vulnerable populations, (ii) vulnerable populations exposed to Urban Heat Island (UHI) effects are more likely to experience energy burden, and (iii) about 21.2–49.4% of population with high energy burden have access to these centers. Considering that more cooling centers are needed to assist energy burdened households alleviate heat exposure impacts, the framework developed herein could be adapted to incorporate other factors (e.g. health impacts, policies) to assess site suitability of existing shelters, identify potential sites for new cooling centers, and geo-target communities where energy efficient emerging technologies could be deployed to reduce heat stress.

58 GEOSCIENCES↗

DOE OSTI2024

Roadmap on nanoscale magnetic resonance imaging

Abstract The field of nanoscale magnetic resonance imaging (NanoMRI) was started 30 years ago. It was motivated by the desire to image single molecules and molecular assemblies, such as proteins and virus particles, with near-atomic spatial resolution and on a length scale of 100 nm. Over the years, the NanoMRI field has also expanded to include the goal of useful high-resolution nuclear magnetic resonance (NMR) spectroscopy of molecules under ambient conditions, including samples up to the micron-scale. The realization of these goals requires the development of spin detection techniques that are many orders of magnitude more sensitive than conventional NMR and MRI, capable of detecting and controlling nanoscale ensembles of spins. Over the years, a number of different technical approaches to NanoMRI have emerged, each possessing a distinct set of capabilities for basic and applied areas of science. The goal of this roadmap article is to report the current state of the art in NanoMRI technologies, outline the areas where they are poised to have impact, identify the challenges that lie ahead, and propose methods to meet these challenges. This roadmap also shows how developments in NanoMRI techniques can lead to breakthroughs in emerging quantum science and technology applications.

Budakian, Raffi↗

DOE OSTI2025

Deep inference of simulated strong lenses in ground-based surveys

The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing systems in ground-based surveys. We present the examination and comparison of two approaches to lens parameter estimation for strong galaxy-galaxy lenses — Neural Posterior Estimation (NPE) and Bayesian Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens models for ground-based imaging data that mimics the Dark Energy Survey (DES). We find that NPE outperforms BNNs, producing posterior distributions that are more accurate, precise, and well-calibrated for most parameters. For the 12-parameter NPE model, the calibration is consistently within <10% of optimal calibration for all parameters, while the BNN is rarely within 20% of optimal calibration for any of the parameters. Similarly, residuals for most of the parameters are smaller (by up to an order of magnitude) with the NPE model than the BNN model. This work takes important steps in the systematic comparison of methods for different levels of model complexity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Deep probabilistic direction prediction in 3D with applications to directional dark matter detectors

Abstract We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere S 2 , giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special case of our loss function when the uncertainty is assumed to be uniform across samples. We develop approximations required to make the likelihood function and gradient calculations stable. The method is applied to the task of predicting the 3D directions of electrons, the most complex signal in a class of experimental particle physics detectors designed to demonstrate the particle nature of dark matter and study solar neutrinos. Using simulated Monte Carlo data, the initial direction of recoiling electrons is inferred from their tortuous trajectories, as captured by the 3D detectors. For 40 keV electrons in a 70% He 30% CO 2 gas mixture at STP, the new approach achieves a mean cosine distance of 0.104 (26 ∘ ) compared to 0.556 (64 ∘ ) achieved by a non-machine learning algorithm. We show that the model is well-calibrated and accuracy can be increased further by removing samples with high predicted uncertainty. This advancement in probabilistic 3D directional learning could increase the sensitivity of directional dark matter detectors.

Computer Science↗

DOE OSTI2025

Elastic dijet production in electron scattering on a longitudinally polarized proton at small x : A portal to orbital angular momentum distributions

We calculate the elastic production of dijets from electron collisions with a longitudinally polarized proton target at small values of the Bjorken x variable. Building on the pioneering proposals of Hatta [Gluon orbital angular momentum at small- x , .] and Bhattacharya [Signature of the gluon orbital angular momentum, ., Probing the quark orbital angular momentum at electron-ion colliders using exclusive π 0 production, ., and Exploring orbital angular momentum and spin-orbit correlation for gluons at the Electron-Ion Collider, .] for measuring the quark and gluon orbital angular momentum (OAM) distributions, our focus is on both the longitudinal double spin asymmetry (DSA) and longitudinal single spin asymmetry (SSA). We compute the numerators of these asymmetries in the small- x formalism of the light-cone operator treatment. Utilizing the small- x expressions for the OAM distributions derived earlier in Kovchegov and Manley [Orbital angular momentum at small x revisited, .], we demonstrate that the DSA provides a robust probe for both the quark and gluon OAM distributions within the proton. In contrast, we find that while the SSA is also sensitive to the OAM distributions, extraction of the latter from the SSA would require new developments in small- x theory and phenomenology, and is probably not feasible at this point in time. These findings highlight the potential of DSA measurements in elastic dijet production at the future Electron-Ion Collider to provide the first-ever direct access to the quark and gluon OAM distributions at small x , paving the way for new insights into the proton spin puzzle. Published by the American Physical Society 2025

Kovchegov, Yuri V. (ORCID:0000000169909173)↗