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DOE OSTI2024

Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials

Using knowledge from statistical thermodynamics and crystallography, we develop an image–image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate–adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. In conclusion, employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R 2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

Sun, Yangzesheng [Univ. of Minnesota, Minneapolis,↗

DOE OSTI2026

Examination of Replicate Syntheses of Metal Organic Frameworks as a Window into Reproducibility in Materials Chemistry

Replicate experiments are a useful tool in understanding the repeatability of scientific measurements. In 2019, a systematic search for replicate syntheses of a collection of 130 metal–organic frameworks (MOFs) found that 89% of these materials had no reported replicate syntheses apart from the original publications identifying the material (Agrawal, M. Proc. Natl. Acad. Sci. U.S.A. 2020, 117, 877−88210.1073/pnas.1918484117). A potential weakness of that search was that only 5–11 years had elapsed since the original publication of each material. Here, this analysis is extended to all publications 11–17 years after the original publication. Although this extended time period identifies more repeat syntheses, 83% of the materials still have no reported replicate syntheses. We also consider how appropriately selected Density Functional Theory (DFT) calculations can provide corroboration for the experimentally reported crystal structures. By using data from previous high-throughput DFT studies, corroborating evidence from DFT was available for 17% of the 130 structures for which no replicate syntheses are available. In total, approximately 1/3 of the 130 MOFs have data associated with replicate synthesis experiments and/or directly corroborating DFT calculations.

Sholl, David S. [Oak Ridge National Laboratory (OR↗

DOE OSTI2026

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

A molecular-level understanding of electrolyte solvation structure and ion–ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation–solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.

MLIPs↗

DOE OSTI2026

Influence of linkage chemistry and side-chain polarity on Ion 2 transport in click-functionalized polymerized ionic liquids.

Post-polymerization functionalization offers precise molecular weight control and enables the high-throughput investigation of structure−property relationships in polymer research. However, post-polymerization functionalization strategies often introduce additional linkage chemistry, and its role in the physical properties of polymerized ionic liquids (PILs) has yet to be explored. In this work, a series of PILs were synthesized using Cu(I)-catalyzed azide−alkyne cycloaddition (CuAAC), with comparison made to N-alkylation substitution chemistry. The triazole ring introduced by CuAAC chemistry was found to induce extensive ion aggregation and deteriorate ion transport. The impact of linkage chemistry on ion transport can be alleviated by incorporating polar ethylene glycol spacers in the side chain, achieving an ionic conductivity of 2.1 × 10−4 S/cm at 30 °C. Furthermore, the effect of polar spacer placement was explored, revealing that overall side-chain polarity, rather than polarity in the vicinity of the ionic group, governs ion aggregation and ion transport in PILs.

Shan, Naisong↗

DOE OSTI2026

Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models

The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.

25 ENERGY STORAGE↗

DOE OSTI2026

AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation

The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput liquid handler. The system engages users in dialogue, decomposes experimental goals into discrete tasks for specialized agents, performs tool-assisted stoichiometric calculations, and iteratively self-corrects its output before generating a hardware-ready file. We present a comprehensive evaluation framework featuring five benchmark experiments of increasing complexity, from simple sample preparation to multi-plate timed syntheses. Through a systematic ablation study of 20 agent configurations, we assess the impact of reasoning capacity, architectural design (single- vs. multi-agent), tool use, and self-correction mechanisms. Our results demonstrate that agent reasoning capacity is the most critical factor for success, reducing quantitative errors in chemical amounts (nRMSE) by over 85% in complex tasks. When combined with a multi-agent architecture and iterative self-correction, AutoLabs approaches expert-authored reference procedures on the benchmark (F1-score > 0.89) on challenging multi-plate syntheses. These findings establish a clear blueprint for developing robust and trustworthy AI partners for autonomous laboratories, highlighting the synergistic effects of modular design, advanced reasoning, and self-correction to ensure both performance and reliability in high-stakes scientific applications. Code: https://github.com/pnnl/autolabs

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Navigating the Path to Autonomy: Real-World Lessons from an Air-Free Self-Driving Laboratory

While autonomous experimentation has promise to accelerate discovery in physcial sciences, the real-world integration of predictive models and experimentation is non-trivial. Here we describe the genesis of a self-driving laboratory (SDL) for air-sensitive chemistry at Argonne National Laboratory and demonstrate the experimental design considerations needed for high-throughput experiments before predictive models can lead to scientific discovery. Our SDL was designed to explore battery electrolyte stability. Our final SDL utilized plate readers in a glovebox with a nitrogen atmosphere to perform kinetic assays and screen hundreds of battery-relevant solvents. However, the roadmap to autonomy and airfree-friendly experimentation required the complex evaluation of several spectroscopic and chromatographic methods. The greatest experimental challenges were (a) developing long-term sampling methods that remained air-free; (b) accelerating kinetics to advance reactivity projections; and (c) ensuring labware compatibility with nonaqueous solvents used in battery chemistry. Our experiences highlight the practical gap between closed-loop aspirations and the realities of chemical discovery, offering lessons on the challenges of transferring every day laboratory workflows to autonomy. These results suggest a more realistic blueprint for autonomy in chemistry—one that balances thoughtful and realistic experimental formulation.

Robertson, Lily A.↗

DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Wide-ranging predictions of new stable compounds powered by recommendation engines

The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.

Science & Technology - Other Topics↗

DOE OSTI2026

Final Technical Report: In-Silico Heterogeneous Catalyst Design for GHG Reduction via Bulk Chemicals

This project has advanced the understanding of ammonia synthesis by developing novel thermochemical catalysts, setting a new benchmark for energy and carbon efficiency in large-scale bulk chemicals production. This research leverages a proprietary computational discovery platform and high-throughput experimental validation, significantly accelerating catalyst optimization and enabling ammonia synthesis at lower temperature and pressure conditions than conventional processes. As a result, green ammonia can be produced at a cost that meets or exceeds established targets, supporting a viable pathway toward decarbonized ammonia for fuel, fertilizers, and hydrogen supply applications.

Grose, Jacob E [Copernic Catalysts, Inc.]↗

DOE OSTI2026

High entropy oxides prediction and discovery by the Mixed Enthalpy-Entropy Descriptor

The vast, high-dimensional composition space of high-entropy oxides (HEOs) offers exceptional opportunities for functional materials discovery, yet it also poses a fundamental challenge: the rational and efficient prediction of stable, synthesizable compositions and the corresponding structure–property relationships. Despite growing interest, the field still lacks broadly applicable, physically grounded descriptors capable of navigating various large chemical spaces. Here, we introduce a Mixed Enthalpy–Entropy Descriptor (MEED) that enables rapid, first-principles–based prediction of HEOs synthesizability across diverse chemistries. Using MEED, we perform high-throughput screening of two distinct HEO families: rocksalt oxides and perovskite oxides. The predicted top candidates in each family were experimentally validated. MEED reveals unifying thermodynamic and structural principles governing stability across both chemical compositions and polymorphs, providing mechanistic insight into the formation of high-entropy phases. This work significantly broadens the accessible chemical design space for HEOs and establishes a data-efficient framework for accelerating the discovery of next-generation functional materials.

Yu, Liping [University of Central Florida]↗

DOE OSTI2026

Open-Air Manufacturing of Efficient Large-Area Perovskite Solar Cells to Meet Stability and Cost Targets

This SETO-funded project (DE-EE0009516) developed a fully open-air manufacturing pathway for perovskite solar cells and modules, eliminating vacuum-based processing steps while improving device stability. Open-air ultrasonic spray deposition, solution combustion synthesis, rapid thermal processing, and atmospheric plasma curing were developed and optimized for all functional device layers — perovskite absorber, hole transport layers (HTLs), electron transport layers (ETLs), transparent conducting oxides (TCOs), and rear electrodes — without reliance on vacuum. Sprayed organic ETLs preserved perovskite crystal structure and enabled power conversion efficiencies exceeding 20%, and open-air processed TCOs and rear electrodes achieved optoelectronic performance comparable to sputtered alternatives at substantially lower manufacturing cost. Device stability was assessed through accelerated aging protocols aligned with IEC and ISOS standards, combined with high-throughput optical diagnostics that identified key degradation mechanisms and antagonistic interactions among heat, humidity, and illumination stressors. A comprehensive technoeconomic analysis quantified the cost and throughput advantages of open-air manufacturing over conventional vacuum-based processes, identifying vacuum deposition as the dominant cost driver and demonstrating a clear pathway toward the $0.02/kWh target. The project produced five peer-reviewed journal publications, eleven conference presentations, two patents/applications, and five Ph.D. dissertations.

14 SOLAR ENERGY↗

DOE OSTI2026

Jumpstart Opportunities to Unleash Leadership in Energy Storage (JOULES)

Current-generation Li-ion batteries with cobalt- and nickel-containing cathodes and graphite anodes are approaching performance and cost limits. In this program, 24M Technologies, Inc. (24M) is teaming with the Massachusetts Institute of Technology (MIT) and University of Michigan (UM) to develop low cost and fast charging sodium metal batteries with good low-temperature performance and high energy density, building upon previous work performed under ARPA-E programs. Key achievements include optimization of solid electrolyte and anode current collector, optimized cathode active materials, development of high-performance electrolyte formulations, and integration of these components into full cells. The cell design incorporates (1) an ultra-thick cathode (>9 mAh/cm 2 ) comprising advanced cobalt-free, sodium cathode active material, (2) advanced fast-charging electrolyte (up to 12 mS/cm) developed using machine learning and automated high-throughput screening technology by UM, and (3) ceramic modified separator that enable smooth Na transport and deposition, developed at MIT, enabling a high-energy density anode-free configuration and maximizing the energy density of sodium batteries. The team has successfully combined these approaches to sodium chemistry and paved the way to meeting the fast-charging, high-energy density, and low-cost requirements of next-generation drone, electric vertical take-off and -landing, and electric vehicle batteries. Performance for anode-free sodium cells developed under this program is more powerful than the commercial Li-ion batteries. The final deliverable cell design has achieved over 300 Wh/kg and volumetric energy density above 800 Wh/L (Table 1). Additionally, the team has achieved over (1) a lifetime of 340 cycles, (2) 80% capacity retention at -20 °C (compared 25 °C), and (3) the ability to fast charge to 80% SOC in 20 minutes.

25 ENERGY STORAGE↗

DOE OSTI2026

High-Temperature SiC Cladding End Plug Irradiation Design and HFIR Readiness

This report documents the design of a High Flux Isotope Reactor (HFIR) irradiation experiment intended to evaluate irradiation effects on the hermeticity of silicon carbide (SiC) end plug specimens under a radial fast neutron flux gradient at representative light-water reactor (LWR) temperatures of approximately 300 °C. The overarching goal of this work is to statistically evaluate SiC end plug hermeticity and mechanical properties following irradiation using the high-throughput irradiation capability discussed here. Each specimen consists of a short section of SiC fiber–reinforced SiC (SiC/SiC) tube with a single monolithic SiC end plug joined to one end. The experiment allows up to 66 specimens to be irradiated in six different stacks within a dry, sealed irradiation capsule derived from the previously developed high-temperature SiC/SiC cladding bowing experiment. The neutronics basis, thermal analysis, and HFIR readiness of the experiment are discussed in this report for two possible design cases. The first design case is based on existing approval documentation and components that are on hand and approved for use, so the experiment insertion would require only specimen receipt, specimen pre-irradiation characterization, experiment assembly, and final fabrication package approval. The second design case provides improved thermal robustness and the preferred end plug geometry but requires fabrication of a modified holder and revisions to the HFIR approval documentation, in addition to the other activities required for the first design case, before insertion.

Hott, Daniel [Oak Ridge National Laboratory (ORNL)↗

DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗

NASA NTRS2025

Assembly of catalytic complexes from randomized oligonucleotides

The early evolution of life relied on catalytic RNAs (ribozymes) for central functions. To test whether early catalysts could have assembled from multiple short nucleic acid fragments in random sequence environments, we performed an in vitro selection from a short RNA library in the presence of 256 different DNA 20-nucleotide oligomers. High-throughput sequencing and biochemical analysis showed that most of the selected 1331 RNA sequences required at least one DNA for activity. Representatives for four of six RNA clusters that depended on DNA cofactors were active even when the 256 DNAs were replaced by completely random DNA 20-nucleotide oligomers. The formation of these catalytic complexes and the recruitment of oligonucleotide cofactors from completely random libraries demonstrate an important principle for the emergence of the earliest oligonucleotide catalysts.

Xu Han↗

DOE OSTI2026

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗

DOE OSTI2024

Thermodynamically informed priors for uncertainty propagation in first-principles statistical mechanics

Here, this work demonstrates how first-principles statistical mechanics approaches within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of 0 K ground states in the material system of interest is incorporated into the prior. The effectiveness of this framework is shown by creating a phase diagram for the fcc zirconium nitride system, including confidence intervals on order-disorder transition temperatures.

Bayesian methods↗