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DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

This is the conference paper accompanying an oral presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗

DOE OSTI2024

Fracture Networks Imaging in CO2 Injection Zones in IBDP Site: An Unsupervised Machine Learning Application with Multiple Datasets

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. This work highlights the integration of unsupervised machine learning (ML) techniques as a transformative tool for advancing understanding of CO2 injection into reservoirs that could potentially contribute to optimizing injection strategies and reservoir management, ultimately bolstering the efficacy and sustainability of CO2 storage.

Kumar, Abhash↗

DOE OSTI2024

Physics Coupled Machine Learning Applications for Geological Carbon Storage

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. In this paper, a physics-based method, CRM is coupled with the advanced artificial intelligence (AI)/machine learning (ML) models in virtual learning environment (VLE) for three-dimension details of reservoir responses and evaluations for a comprehensive understanding for CCS field operations and reservoir managements.

Liu, Guoxiang↗

DOE OSTI2024

Developing a Transferable Framework for CO2-Stimulated Geothermal Energy Enhancement: A Case Study

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. In this paper, we present innovative technologies to image key features, including CO₂-stimulated fracture imaging, CO₂ fluid sweep imaging and heat transfer and exchange imaging by leveraging multiple datasets and applying advanced AI/ML, multi-level data analytics, and data/information fusion to better understand the geothermal reservoir for enhanced recovery.

Liu, Guoxiang↗

DOE OSTI2025

Workflow for Process Automation of Soil Gas Results from an Automated Soil Gas-Sampling System for Application in Carbon Storage Projects

Extended abstract for Geoconvention, Calgary, Alberta, Canada, May 12–14, 2025. The Energy & Environmental Research Center (EERC) developed an automated workflow for processing soil gas measurements collected from the automated soil gas-sampling systems deployed across the project site. Raw soil gas measurements are collected from each station every 4 hours and automatically uploaded to a cloud database. The workflow begins by writing code to download the data to a workstation automatically, then the data are published to an online dashboard that visualizes the measurements in time-series plots and a process-based decision-making framework. This automated workflow accelerates the time from data acquisition to decision-making. It supports carbon storage project operators by preparing and delivering a live, standardized dataset for quick analysis and source attribution to provide assurance of containment and overall permit compliance.

02 PETROLEUM↗

DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Fermi National Accelerator Laboratory

Particle physicists aim to discover what the universe is made of and how it works. They study the smallest building blocks of matter using some of the largest and most complex machines in the world. Fermilab hosts a range of cutting-edge experiments and develops and builds technologies that support particle physics research at locations around the world, including deep underground laboratories in South Dakota and Canada, the Large Hadron Collider in Europe, and the South Pole Telescope. Together with our international partners, we expand humankind’s understanding of matter, energy, space and time, capturing imaginations and inspiring future generations.

Fermilab, Fermilab↗

DOE OSTI2026

All Systems Go: Regional Collaborations for Scaling AEC Innovation: Preprint

The high and rising cost of preserving and delivering housing in the U.S. requires changes to existing practices of finance, design, and construction. Innovative methods such as industrialized construction could offer the means to address housing undersupply while reducing delivery costs, operational costs and material waste in the building industry, but they face challenges to success and to scale. Simultaneously, construction and cleantech innovators themselves face skepticism from the traditional entrepreneurial ecosystem such as incubators and accelerators while attempting to navigate systems level challenges. To respond to this need, various public and private sector stakeholders have launched initiatives to support innovative companies. These include nonprofits such as Terner Labs and Ivory Innovations offering curated programming to architecture, engineering, and construction (AEC) startups; housing developers in Minnesota and California "bundling" multiple projects together to reach economies of scale with a consistent project team; public and private sector entities developing "catalogues" of pre-approved home designs in the U.S. and Canada. This exploratory paper documents several of these emerging ecosystem-development efforts to support innovative housing approaches, characterizing them by leading stakeholder and intervention strategy based on publicly available information. The paper finds that these initiatives share similar high level goals but vary in implementation, reflecting different stakeholder priorities, regional market and policy dynamics, and housing typologies. The early stage of these efforts offer limited data for comparing actual outcomes, but the paper highlights common qualitative themes and identifies opportunities for further research and potential coordination among these efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Beyond-classical computation in quantum simulation

Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful and meaningful problems, remains a central challenge. Here, we show that superconducting quantum annealing processors can rapidly generate samples in close agreement with solutions of the Schrödinger equation. We demonstrate area-law scaling of entanglement in the model quench dynamics of two-, three-, and infinite-dimensional spin glasses, supporting the observed stretched-exponential scaling of effort for matrix-product-state approaches. We show that several leading approximate methods based on tensor networks and neural networks cannot achieve the same accuracy as the quantum annealer within a reasonable time frame. Thus, quantum annealers can answer questions of practical importance that may remain out of reach for classical computation.

King, Andrew D. [D-Wave Quantum Inc., Burnaby, BC ↗

DOE OSTI2025

Investigation of the excited states of 114 Sn using the GRIFFIN spectrometer at TRIUMF

The semi-magic 110-122 Sn isotopes display signs of shape coexistence in their excited 0 + states, which, in contrast to the spherical 0+ ground states, are deformed. Here, this paper investigates the nuclear structure of 114 Sn using the competing β + decay and electron capture of a radioactive beam of 114 Sb produced at the TRIUMF-ISAC facility using the GRIFFIN spectrometer. This study will allow for an in-depth understanding of the excited 0 + states in 114 Sn, by focusing on their decay patterns. In the present experiment, transitions at 856.2-keV and 1405.0-keV, which were observed in an earlier β + decay study but not placed in the 114 Sn level scheme, have been Physics, assigned to the level scheme in connection to the 0$^+_3$ level at 2156.0-keV. Properly assigning these transitions refines the level scheme and enhances our understanding of the nuclear structure in 114 Sn.

GRIFFIN↗

DOE OSTI2025

Precision Mass Measurements Reveal Low Neutron Pairing in Tin beyond 𝑁=82 and Its Impact on Stellar Nucleosynthesis

We present a study on neutron-rich tin (𝑍 =50) isotopes beyond the doubly closed shell of 𝑁 = 82 through high-precision mass measurements, including the first-ever measurements of the masses of 136 Sn, 137 Sn, and 138 Sn isotopes. These measurements enhance our understanding of the nuclear structure and astrophysical nucleosynthesis in this previously unexplored region. The new mass data are used for evaluation of the final abundances of mass numbers 𝐴 =135 and 137 in 𝑟-process network calculations. Our findings reveal a notable change in the empirical pairing gap for tin isotopes beyond the 𝑁 = 82 closed shell and a shift in the two-neutron-separation energy slope compared to heavier elements above the shell closure. A new set of ab initio calculations effectively describes these observed trends.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Exploring the onset of collectivity approaching 𝑁=40 with masses of manganese isotopes

Isotopes in the region of the nuclear chart below 68 Ni have been the subject of intense experimental and theoretical effort due to the potential onset of a new “island of inversion” when crossing the harmonic oscillator subshell closure at 𝑁=40. Here, we have measured the masses of 64−68 Mn using TITAN's multiple-reflection time-of-flight mass spectrometer, resulting in the first precision mass measurements of 67 Mn and 68 Mn. These results are compared to ab initio calculations and modern shell model calculations and show an increase in collectivity approaching 𝑁=40.

binding energy & masses↗

DOE OSTI2024

Intrinsic and environmental drivers of pairwise cohesion in wild Canis social groups

Animals within social groups respond to costs and benefits of sociality by adjusting the proportion of time they spend in close proximity to other individuals in the group (cohesion). Variation in cohesion between individuals, in turn, shapes important group-level processes such as subgroup formation and fission–fusion dynamics. Although critical to animal sociality, a comprehensive understanding of the factors influencing cohesion remains a gap in our knowledge of cooperative behavior in animals. We tracked 574 individuals from six species within the genus Canis in 15 countries on four continents with GPS telemetry to estimate the time that pairs of individuals within social groups spent in close proximity and test hypotheses regarding drivers of cohesion. Pairs of social canids (Canis spp.) varied widely in the proportion of time they spent together (5%–100%) during seasonal monitoring periods relative to both intrinsic characteristics and environmental conditions. The majority of our data came from three species of wolves (gray wolves, eastern wolves, and red wolves) and coyotes. For these species, cohesion within social groups was greatest between breeding pairs and varied seasonally as the nature of cooperative activities changed relative to annual life history patterns. Across species, wolves were more cohesive than coyotes. For wolves, pairs were less cohesive in larger groups, and when suitable, small prey was present reflecting the constraints of food resources and intragroup competition on social associations. Pair cohesion in wolves declined with increased anthropogenic modification of the landscape and greater climatic variability, underscoring challenges for conserving social top predators in a changing world. We show that pairwise cohesion in social groups varies strongly both within and across Canis species, as individuals respond to changing ecological context defined by resources, competition, and anthropogenic disturbance. Our work highlights that cohesion is a highly plastic component of animal sociality that holds significant promise for elucidating ecological and evolutionary mechanisms underlying cooperative behavior.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

El Agente: An autonomous agent for quantum chemistry

Computational chemistry tools are widely used to study the behavior of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work, we introduce El Agente Q, an LLM-based multi-agent system that dynamically generates and executes quantum chemistry workflows from natural language user prompts. The system is built on a novel cognitive architecture featuring a hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, post-analysis, and autonomous file handling and submission. El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance (averaging >87% task success) and adaptive error handling through in situ debugging. It also supports longer-term, multi-step task execution for more complex workflows, while maintaining transparency through detailed action trace logs. Together, these capabilities lay the foundation for increasingly autonomous and accessible quantum chemistry.

agentic systems↗

DOE OSTI2025

Signature of 0 + excited state and shape coexistence in 94 Kr through 93 Kr(d,p) 94 Kr reaction

A measurement of the excitation spectrum of 94 Kr via one-neutron transfer to the ground state of 93 Kr using the 93 Kr(d,p) 94 Kr reaction at 8A MeV, observing the outgoing protons, performed with the IRIS facility at TRIUMF is reported. Two states in 94 Kr, at 1.50±0.14 MeV and 2.20±0.14 MeV, were observed. An adiabatic-wave approximation analysis of the differential cross sections leads us to identify the lower energy state as being populated with neutron transfer to the 3 s1/2 orbital. This leads to the first observation of the lowest 0 + excited state in 94 Kr, hence signaling shape co-existence. Theoretical calculations performed within the in-medium similarity renormalization group framework are presented that also show the existence of a low-energy 0 + state, aligning qualitatively with the observation.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2024

Ion Manipulation from Liquid Xe to Vacuum: Ba-Tagging for a nEXO Upgrade and Future 0 νββ Experiments

Neutrinoless double beta decay (0𝜈𝛽𝛽) provides a way to probe physics beyond the Standard Model of particle physics. The upcoming nEXO experiment will search for 0𝜈𝛽𝛽 decay in 136 Xe with a projected half-life sensitivity exceeding 10 28 years at the 90% confidence level using a liquid xenon (LXe) Time Projection Chamber (TPC) filled with 5 tonnes of Xe enriched to ∼90% in the 𝛽𝛽-decaying isotope 136 Xe. In parallel, a potential future upgrade to nEXO is being investigated with the aim to further suppress radioactive backgrounds and to confirm 𝛽𝛽-decay events. This technique, known as Ba-tagging, comprises extracting and identifying the 𝛽𝛽-decay daughter 136 Ba ion. One tagging approach being pursued involves extracting a small volume of LXe in the vicinity of a potential 𝛽𝛽-decay using a capillary tube and facilitating a liquid-to-gas phase transition by heating the capillary exit. The Ba ion is then separated from the accompanying Xe gas using a radio-frequency (RF) carpet and RF funnel, conclusively identifying the ion as 136 Ba via laser-fluorescence spectroscopy and mass spectrometry. Simultaneously, an accelerator-driven Ba ion source is being developed to validate and optimize this technique. The motivation for the project, the development of the different aspects, along with the current status and results, are discussed here.

a-tagging↗

DOE OSTI2025

Biofluid-based staging of Alzheimer’s disease

Recently, conceptual systems for the in vivo staging of Alzheimer’s disease (AD) using fluid biomarkers have been suggested. Thus, it is important to assess whether available fluid biomarkers can successfully stage AD into clinically and biologically relevant categories. In the TRIAD cohort, we explored whether p-tau217, p-tau205 and NTA-tau (biomarkers of early, intermediate and late AD pathology, respectively) have potential for biofluid-based staging in cerebrospinal fluid (CSF; n = 219) and plasma (n = 150), and compared them in a paired CSF and plasma subset (n = 76). Our findings suggest a good concordance between biofluid staging and underlying pathology when classifying amyloid-positivity into three categories based on neurofibrillary pathology: minimal/non-existent (p-tau217 positive), early-to-intermediate (p-tau217 and p-tau205 positivity), and advanced tau tangle deposition (p-tau217, p-tau205 and NTA-tau positive), as indexed by tau-PET. Discordant cases accounted for 4.6% and 13.3% of all CSF and plasma measurements respectively (9.2% and 11.8% in paired samples). Notably, CSF- and plasma-based staging matched one another in 61.7% of the cases, while approximately 32% of the remaining participants were one to three biofluid stages higher in CSF as compared to plasma. Overall, these exploratory results suggest that biofluid staging of AD holds potential for offering valuable insights into underlying AD hallmarks and disease severity. However, its applicability beyond molecular characterization at research settings has yet to be demonstrated.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Confinement Reconstruction Unlocks Stable Ru Single Atom-Doped IrO x Anodes for Long-Term High-Rate CO 2 Electrolysis

IrO 2 is a commonly employed anode catalyst for CO 2 electrolysis in membrane electrode assembly (MEA) systems. However, under high current densities, its structural reconstruction leads to activity loss and stability degradation, limiting the industrial viability of CO 2 electrolysis. In this work, we demonstrated a confinement reconstruction strategy to precisely regulate the structural evolution during electrolysis. Ethylene glycol serves as a structural modulator, protecting the catalyst surface, suppressing soluble species formation, and promoting ordered structural evolution. Single-atom Ru acts as a stability enhancer, forming robust Ir–O–Ru bridging structures that facilitate an ordered transformation from a 4-fold [RuO 4 ]/[IrO 4 ] to a 6-fold symmetry [RuO 6 ]/[IrO 6 ] octahedral framework, thereby enhancing structural rigidity and long-term stability. As a result, in MEA-based CO 2 electrolysis, the catalyst achieves a stable operation at 200 mA cm –2 for 480 h, maintaining a CO selectivity above 80%. Theoretical calculations further elucidate that the enhanced stability originates from the suppression of oxygen vacancy formation, making the lattice-oxygen-mediated mechanism (LOM) potentially less favorable. This work provides insights into the structural evolution of the OER catalysts under high-current-density conditions, paving the way for large-scale CO 2 electrolysis commercialization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗