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DOE OSTI2026

Hydrology in the Age of Artificial Intelligence: From Fragmentation to Coherent Terrestrial Hydrosphere Science

The rapid rise of machine learning (ML) in hydrology has prompted debate about the discipline's scientific relevance. While ML often outperforms traditional models in streamflow prediction, we argue that this reflects a deeper limitation: persistent fragmentation of hydrological science itself. Narrow focus on isolated components has hindered the development of coherent, scale‐relevant understanding of the integrated terrestrial hydrosphere. This is illustrated, for example, by widely divergent estimates of groundwater–streamflow interactions and of water balance‐implied ongoing storage changes. We argue that hydrology's future lies not in choosing between ML and physics, but in integrating data‐driven and process‐based approaches to advance consistent, realistic, and societally relevant understanding of the terrestrial hydrosphere and its multifaceted roles in the Earth System.

Painter, Scott L. [Oak Ridge National Laboratory (↗

DOE OSTI2025

Science acceleration and accessibility with self-driving labs

In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs’ potential to enhance human–machine and human–human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects.

Canty, Richard B. [North Carolina State University↗

DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗

DOE OSTI2025

Applications of visualization technology in the structural sciences

The structural sciences are undergoing a transformation driven by advancements in visualization technologies that aid researchers in understanding and communicating experimental data from complex molecular systems. New applications of integrative structural biological and biophysical approaches add a wide variety of complementary information from a broad range of scientific disciplines. These approaches extend structural biophysical methodologies to enable research by the incorporation of a variety of data streams and utilization of tools like molecular graphics, virtual reality, and machine learning. To redefine how structural data—particularly from cryo-electron microscopy and x-ray crystallography—are fed forward for scientific exploration and communication, the advances in tools for data visualization and interpretation have been critical. By bringing molecular systems into an interactive three-dimensional space, these novel technologies enhance research workflows, facilitate structure-based drug design, and create engaging educational experiences. Taken together, these visualization innovations are essential tools for advancing the field by making concepts more accessible and compelling.

Eng, Edward T. [New York Structural Biology Center↗

DOE OSTI2024

A compact and portable gamma-ray spectrometer (GRASP) for inertial confinement fusion and basic science experiments

A compact and portable gamma-ray spectrometer has been designed to diagnose different components of the inertial confinement fusion-relevant γ-ray spectrum with energies between ∼3.7–17.9 MeV. The system is designed to be as compact as possible for convenient transportation and fielding in diagnostic ports on the OMEGA laser, the National Ignition Facility, and other photon-source facilities. The system consists of a conversion foil for Compton scattering in front of four magnetic spectrometer “arms,” each covering a different energy range and constructed out of cylindrical permanent magnet Halbach arrays. Monte Carlo simulations have been used to optimize and assess the performance of the conversion foil, and COSY INFINITY ion-optical simulations have been used to optimize the spectrometer magnets. The performance of the design is assessed for a simulated direct-drive γ-ray spectrum. Spanning its total γ-ray energy bandwidth and using a 1.7 mm thick boron conversion foil, the system’s total energy resolution and efficiency are ∼15.8%–4.5% and 5.4 × 10−7–3.7 × 10−7e−/γ, respectively, with room for improvement. Spectral γ-ray measurements will provide guidance to the inertial confinement fusion program toward achieving high-energy gain relevant to inertial fusion energy and enable new measurement capabilities for basic discovery science.

Instruments & Instrumentation↗

DOE OSTI2025

Establishment of a Nationwide Plasma Science and Fusion Energy Certification and Apprenticeship Programs

Here we propose the establishment of a national Plasma Science and Fusion Energy training program that will combine professional development certification courses with hands-on apprenticeship opportunities. The program should be developed by a collaboration of stakeholders (academia, national laboratories, and industry) and should offer pathways for students from community colleges (CCs), minority-serving institutions (MSIs), high school graduates, and veterans. We argue that certification courses offer fast and flexible discipline-specific education that can be rapidly translated into marketable skills through hands-on apprenticeship opportunities. Furthermore, we first list major findings and recommendations related to the fusion workforce and discuss how to address them through the establishment of the proposed training program. We then highlight the appropriateness of this format for engagement with CCs, MSIs, high schools, and veteran employment services. In the final section, we provide a strategy for the establishment of the program, along with a tentative timeline and projected costs.

Certification↗

DOE OSTI2025

The Simons Observatory: science goals and forecasts for the enhanced Large Aperture Telescope

We describe updated scientific goals for the wide-field, millimeter-wave survey that will be produced by the Simons Observatory (SO). Significant upgrades to the 6-meter SO Large Aperture Telescope (LAT) are expected to be complete by 2028, and will include a doubled mapping speed with 30,000 new detectors and an automated data reduction pipeline. In addition, a new photovoltaic array will supply most of the observatory's power. The LAT survey will cover about 60% of the sky at a regular observing cadence, with five times the angular resolution and ten times the map depth of the Planck satellite. The science goals are to: (1) determine the physical conditions in the early universe and constrain the existence of new light particles; (2) measure the integrated distribution of mass, electron pressure, and electron momentum in the late-time universe, and, in combination with optical surveys, determine the neutrino mass and the effects of dark energy via tomographic measurements of the growth of structure at redshifts z ≲ 3; (3) measure the distribution of electron density and pressure around galaxy groups and clusters, and calibrate the effects of energy input from galaxy formation on the surrounding environment; (4) produce a sample of more than 30,000 galaxy clusters, and more than 100,000 extragalactic millimeter sources, including regularly sampled AGN light-curves, to study these sources and their emission physics; (5) measure the polarized emission from magnetically aligned dust grains in our Galaxy, to study the properties of dust and the role of magnetic fields in star formation; (6) constrain asteroid regoliths, search for Trans-Neptunian Objects, and either detect or eliminate large portions of the phase space in the search for Planet 9; and (7) provide a powerful new window into the transient universe on time scales of minutes to years, concurrent with observations from the Vera C. Rubin Observatory of overlapping sky.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Target development using the method of High-Intensity Vibrational Powder Plating (HIVIPP) at the Center for Accelerator Target Science (CATS) at Argonne National Laboratory (ANL)

One of the primary goals of the Center for Accelerator Target Science (CATS) is to provide targets and foils in support of the ATLAS User Facility and the Low-Energy community at large. While a wide array of target production techniques are available at CATS, new methods that must be explored invariably arise. One such technique, the High-Intensity Vibrational Powder Plating (HIVIPP), was first reported in 1997 by Isao Sugai. It was developed to produce targets and stripper foils that were difficult to make by standard methods. At Argonne National Laboratory (ANL), we have successfully constructed and tested a simple system for this purpose. We have produced targets of carbon and titanium on various metal backings using the HIVIPP method. We are currently in the exciting phase of exploring the production of other elements, including isotopically enriched and radioactive material. This work is in progress and will be further detailed with specific examples.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Maven: a multimodal foundation model for supernova science

Abstract A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5 M synthetic supernovae using a contrastive objective. We then fine-tune the model on 4702 observed supernovae from the Zwicky transient facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin observatory, Maven will serve as a valuable tool for leveraging large, unlabeled and multimodal time-domain datasets.

Zhang, Gemma (ORCID:0000000280198082)↗

DOE OSTI2024

Network science can improve the sustainable development of solar energy

Abstract The recent emergence of agrivoltaic and ecovoltaic approaches to ground-mounted photovoltaic (PV) energy provides a much-needed alternative to the environmentally disruptive practices employed in utility-scale solar development. Research on such land-sharing approaches has grown rapidly, with an emphasis on characterizing how PV arrays impact ecosystem processes and agricultural productivity. Although these studies have done well to quantify a variety of dual-use solar practices by employing site-specific sampling designs, this approach has limited our ability to synthesize results across sites, regions, and globally. We call for a network science approach for improved cross-site synthesis of dual-use solar research. We contend that a common approach for data collection and synthesis will facilitate a more rigorous investigation of the agricultural and ecological impacts of PV development across space and over time. The products of this scientifically informed approach can be directly applied to improve sustainable land management.

Bacon, Taylor (ORCID:0009000518578569)↗

DOE OSTI2025

Electromagnetic modeling and science reach of DMRadio-m 3

DMRadio-m 3 is an experimental search for dark matter axions. It uses a solenoidal dc magnetic field to convert an axion dark-matter signal to an ac electromagnetic response in a coaxial copper pickup. The current induced by this axion signal is measured by dc SQUIDs. DMRadio-m 3 is designed to be sensitive to Kim-Shifman-Vainshtein-Zakharov (KSVZ) and Dine-Fischler-Srednicki-Zhitnisky (DFSZ) QCD axion models in the 10–200 MHz (41 neV/𝑐 2 –0.83 μ⁢eV/𝑐 2 ) range, and to axions with 𝑔 𝑎⁢𝛾⁢𝛾 =𝑔 𝑎⁢𝛾⁢𝛾,DFSZ ⁡(30 MHz) =1.87 ×10 −17 GeV −1 over 5–30 MHz as an extended goal. In this work, we present the electromagnetic modeling of the response of the experiment to an axion signal over the full frequency range of DMRadio-m 3 , which extends from the low-frequency, lumped-element limit to a regime where the axion Compton wavelength is only a factor of 2 larger than the detector size. With these results, we determine the live time and sensitivity of the experiment. The primary science goal of sensitivity to DFSZ axions across 30–200 MHz can be achieved with a 3⁢𝜎 live scan time of 2.9 years.

Dark matter direct detection↗

DOE OSTI2024

Towards Resilient Near Real-Time Analysis Workflows in Fusion Energy Science

Nuclear fusion holds the promise of an endless source of energy. Several research experiments across the world and joint modeling and simulation efforts between the nuclear physics and high performance computing communities are actively preparing the operation of the International Thermonuclear Experimental Reactor (ITER). Both experimental reactors and their simulated counterparts generate data that must be analyzed quickly and in a resilient way to support decision making for the configuration of subsequent runs or prevent a catastrophic failure. However, the cost if the traditional techniques used to improve the resilience of analysis workflows, i.e., replicating datasets and computational tasks, becomes prohibitive with explosion of the volume of data produced by modern instruments and simulations. Therefore, we advocate in this paper for an alternate approach based on data reduction and data streaming. The rationale is that by allowing for a reasonable, controlled, and guaranteed loss of accuracy it becomes possible to transfer smaller amounts of data, shorten the execution time of analysis workflows, and lower the cost of replication to increase resilience. We develop our research and development roadmap towards resilient near real-time analysis workflows in fusion energy science and present early results showing that data streaming and data reduction is a promising way to speed up the execution and improve the resilience of analysis workflows.

Suter, Fred↗

DOE OSTI2025

Privacy Preservation from High-Performance Computing to Autonomous Science [Industrial and Governmental Activities]

High-Performance Computing (HPC) and Leadership-Class Supercomputing are driving forces behind scientific advancements, enabling researchers to tackle complex challenges in physics, chemistry, biology, and engineering. These systems power vast simulations and data analyses, fueling discoveries in fields ranging from materials science to climate modeling. However, their use often involves processing sensitive data—such as proprietary industry simulations, biomedical records, and national security computations—posing significant privacy concerns. In conclusion, this issue is amplified in collaborative environments like Department of Energy (DOE) user facilities, where HPC resources are shared across institutions to foster innovation.

Kotevska, Olivera [Oak Ridge National Laboratory (↗

DOE OSTI2024

A Microservices Architecture Toolkit for Interconnected Science Ecosystems

Microservices architecture is a promising approach for developing reusable scientific workflow capabilities for inte- grating diverse resources, such as experimental and observational instruments and advanced computational and data management systems, across many distributed organizations and facilities. In this paper, we describe how the INTERSECT Open Architec- ture leverages federated systems of microservices to construct interconnected science ecosystems, review how the INTERSECT software development kit eases microservice capability develop- ment, and demonstrate the use of such capabilities for deploying an example multi-facility INTERSECT ecosystem.

Brim, Michael↗

DOE OSTI2024

PANDA-FES: Portable and Adaptable Neutron Diagnostics for Advancing Fusion Energy Science

Nuclear fusion is a potential source of carbon-free electricity with many concepts in development. The Portable and Adaptable Neutron Diagnostics for Advancing Fusion Energy Science (PANDA-FES) suite has been deployed since 2021 to measure neutron yield, energy, and spatiotemporal source location at two different Z-pinch fusion devices. This diagnostic can be used at a variety of facilities pursuing fusion in the magnetic, inertial, and magneto-inertial regimes. These different regimes have a wide range of time scales from less than 100 ns to a few μ s, neutron yields from 10 6 to 10 11 , and noise environments. Neutron yield is measured through activation of 79 Br and 89 Y with calibrated detectors. Temporal, spatial, and energy dependence of neutrons is measured with scintillators coupled to photomultiplier tubes (PMTs). Experimental setups and data analysis methods have been developed for these conditions. Finally, neutron yield, neutron energy anisotropy, and spatiotemporal evolution of the source have been measured.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

From breaking rules to making rules in materials science

This editorial is a perspective article discussing the broader philosophy of synthesis science, emphasizing how techniques such as MBE allow researchers to manipulate bonding, structure, and defects beyond equilibrium thermodynamics, enabling the design of new materials and emergent properties through controlled growth and epitaxial engineering.

Jalan, Bharat [Univ. of Minnesota, Minneapolis, MN↗

DOE OSTI2024

Citizen science for IceCube: Name that Neutrino

Name that Neutrino is a citizen science project where volunteers aid in classification of events for the IceCube Neutrino Observatory, an immense particle detector at the geographic South Pole. From March 2023 to September 2023, volunteers did classifications of videos produced from simulated data of both neutrino signal and background interactions. Name that Neutrino obtained more than 128,000 classifications by over 1800 registered volunteers that were compared to results obtained by a deep neural network machine-learning algorithm. Possible improvements for both Name that Neutrino and the deep neural network are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science

Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists. Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem. However, it is unclear how this new capability would materialize and integrate in the real world. To address this, we propose a conceptual framework where workflows evolve along two dimensions which are intelligence (from static to intelligent) and composition (from single to swarm) to chart an evolutionary path from current workflow management systems to fully autonomous scientific laboratories. With these trajectories in mind, we present an architectural blueprint that can help the community take the next steps towards harnessing the opportunities in autonomous science with the potential for 100x discovery acceleration and transformational scientific workflows.

Shin, Woong [ORNL] (ORCID:0000000172077814)↗