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DOE OSTI2026

Pre-training Vision Models for the Classification of Alerts from Wide-field Time-domain Surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Confronting Earth System Model trends with observations

Anthropogenically forced climate change signals are emerging from the noise of internal variability in observations, and the impacts on society are growing. For decades, Climate or Earth System Models have been predicting how these climate change signals will unfold. While challenges remain, given the growing forced trends and the lengthening observational record, the climate science community is now in a position to confront the signals, as represented by historical trends, in models with observations. This review covers the state of the science on the ability of models to represent historical trends in the climate system. It also outlines robust procedures that should be used when comparing modeled and observed trends and how to move beyond quantification into understanding. Finally, this review discusses cutting-edge methods for identifying sources of discrepancies and the importance of future confrontations.

58 GEOSCIENCES↗

DOE OSTI2022

Double and Charge-Transfer Excitations in Time-Dependent Density Functional Theory

Time-dependent density functional theory has emerged as a method of choice for calculations of spectra and response properties in physics, chemistry, and biology, with its system-size scaling enabling computations on systems much larger than otherwise possible. While increasingly complex and interesting systems have been successfully tackled with relatively simple functional approximations, there has also been increasing awareness that these functionals tend to fail for certain classes of approximations. Here I review the fundamental challenges the approximate functionals have in describing double excitations and charge-transfer excitations, which are two of the most common impediments for the theory to be applied in a black-box way. At the same time, I describe the progress made in recent decades in developing functional approximations that give useful predictions for these excitations.

Chemistry↗

DOE OSTI2020

Power Flow Spectroscopy Diagnostics & Platform Development at the Z Pulsed Power Facility

Large pulsed power accelerators deliver multi-MJ pulses of electrical energy to a variety of high energy density (HED) physics experiments that support stockpile science programs. Understanding the plasma formation mechanisms and resulting electrical power transport (or "power flow") in the vacuum magnetically insulated transmission lines (MITLs) is an important area of ongoing research, and could provide a means to improve the performance of today's pulsed power accelerators while improving confidence in the design options for next-generation pulsed power concepts. Power flow science has been studied for decades, but these studies have not provided a predictive understanding of plasma formation and expansion in MITL systems. Several recent factors in pulsed power system design have generated a renewed (and urgent) interest in developing validated, multi-physics power flow engineering models with increased scrutiny and understanding. Examples of these factors include (i) the use of high inductance experimental configurations that could increase current "loss", (ii) interest in long-pulse applications that require predictable pulse shapes, and (iii) the desire to develop a deeper understanding of how current loss phenomena scale to larger accelerator configurations. This work is directed to support the validation of multi-physics power flow engineering models required to realize pulsed power systems for the NNSA mission.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Material Control and Accounting for Liquid-Fueled Molten Salt Reactors: Material Control and Holdup Considerations

The US Nuclear Regulatory Commission (NRC) will likely require license applicants for liquid-fueled molten salt reactors (MSRs) with circulating fuel to submit a nuclear material control and accounting (MC&A) plan or detailed MC&A program description for the facility. In liquid-fueled MSRs with special nuclear material (SNM) in bulk (i.e., not in discrete items) form and rapidly changing quantities due to fuel transmutation and depletion, using traditional nuclear material accounting methods with material balance evaluations is challenging. In reactors with changing inventories, these expected quantities of SNM must be calculated based on operational parameters. Reducing uncertainties on these expected quantities is challenging in the case of MSRs without decades of operational experience to verify and validate predictive computational codes. Moreover, many areas in MSRs are inaccessible because of high-temperature and high-radiation environments, making measurements challenging.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Quantifying Uncertainty in HPC Job Queue Time Predictions

High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.

HPC↗

DOE OSTI2022

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Decadal Variability of Southeast US Rainfall in an Eddying Global Coupled Model

Abstract Ocean variability is a dominant source of remote rainfall predictability, but in many cases the physical mechanisms driving this predictability are not fully understood. This study examines how ocean mesoscales (i.e., the Gulf Stream SST front) affect decadal Southeast US (SEUS) rainfall, arguing that the local imprint of large‐scale teleconnections is sensitive to resolved mesoscale features. Based on global coupled model experiments with eddying and eddy‐parameterizing ocean, we find that a resolved Gulf Stream improves localized rainfall and remote circulation response in the SEUS. The eddying model generally improves the air‐sea interactions in the Gulf Stream and the North Atlantic Subtropical High that modulate SEUS rainfall over decadal timescales. The eddy‐parameterizing simulation fails to capture the sharp SST gradient associated with the Gulf Stream and overestimates the role of tropical Pacific SST anomalies in the SEUS rainfall.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

A Statistician’s Overview of Physics-Informed Neural Networks for Spatio-Temporal Data

The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that include mechanistic information, such as would be available from partial differential equations, with a strong focus on the quantification of uncertainty associated with data, process, and parameters. Here, we give a brief review of several paradigms and focus our attention on Bayesian implementations given they naturally accommodate uncertainty quantification. We then show that it is straight-forward to include the Bayesian PINN (B-PINN) within the Bayesian hierarchical model (BHM) framework that has long been considered for modeling dynamic spatio-temporal processes. Such a BHM-PINN is illustrated via a simulation study in which a latent nonlinear Burgers’ equation PDE governs the dynamics of Poisson distributed spatio-temporal data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian↗

DOE OSTI2023

Multi-decadal variation of ENSO forecast skill since the late 1800s

Diagnosing El Niño-Southern Oscillation (ENSO) predictability within operational forecast models is hindered by computational expense and the need for initialization with three-dimensional fields generated by global data assimilation. We instead examine multi-year ENSO predictability since the late 1800s using the model-analog technique, which has neither limitation. We first draw global coupled model states from pre-industrial control simulations, from the Coupled Model Intercomparison Project Phase 6, that are chosen to initially match observed monthly sea surface temperature and height anomalies in the Tropics. Their subsequent 36-month model evolution are the hindcasts, whose 20th century ENSO skill is comparable to twice-yearly hindcasts generated by a state-of-the-art European operational forecasting system. Despite the so-called spring predictability barrier, present throughout the record, there is substantial second-year ENSO skill, especially after 1960. Overall, ENSO exhibited notably high values of both amplitude and skill towards the end of the 19th century, and again in recent decades.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Forecasting Remote Atmospheric Responses to Decadal Kuroshio Stability Transitions

Based on observational estimates and global ocean eddy-resolving coupled retrospective initialized predictions, we show that Kuroshio Extension variability affects rainfall variability along the west coast of North America. We show that the teleconnection between the current undulations and downstream rainfall can lead to improved subseasonal to seasonal predictions of precipitation over California, and we demonstrate that capturing these teleconnections requires coupled systems with sufficient ocean resolution (i.e., eddy-resolving), especially over time scales longer than one season. The improved forecast skill is diagnosed in terms of 35 years of retrospective initialized ensemble forecasts with an ocean eddy-resolving and an ocean eddy-parameterized coupled model. Not only does the ocean eddy-resolving model show sensitivity to Kuroshio Extension variability in terms of western North America precipitation, but the ocean eddy-resolving forecasts also show improved forecast skill compared to the ocean eddy-parameterized model. Additionally, the ocean eddy-parameterized coupled model shows no sensitivity to Kuroshio Extension variability. We also find near-decadal variability associated with a progression of a lower-tropospheric height dipole around the North Pacific and how these height anomalies lead to wind-driven Rossby waves that affect the eddy activity in the Kuroshio Extension with a time lag on the order of four years. This decadal-scale variability (~10 years) opens the possibility of multiyear predictability of western North American rainfall.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Ecological acclimation: A framework to integrate fast and slow responses to climate change

Ecological responses to climate change occur across vastly different time-scales, from minutes for physiological plasticity to decades or centuries for community turnover and evolutionary adaptation. Accurately predicting the range of ecosystem trajectories will require models that incorporate both fast processes that may keep pace with climate change and slower ones likely to lag behind and generate disequilibrium dynamics. However, the knowledge necessary for this integration is currently fragmented across disciplines. We develop ‘ecological acclimation’ as a unifying framework to emphasize the similarity of dynamics driven by processes operating on dramatically different time-scales and levels of biological organization. The framework focuses on ecoclimate sensitivities, measured as the change in an ecological response variable per unit of climate change. Acclimation processes acting at different time-scales cause these sensitivities to shift in magnitude and even direction over time. We highlight shifting ecoclimate sensitivities in case studies from diverse ecosystems, including terrestrial plant communities, coral reefs and soil microbiomes. Models predicting future ecosystem states inevitably make assumptions about acclimation processes; these assumptions must be explicit for users to evaluate whether a model is appropriate for a given forecast horizon. Similarly, decision frameworks that clearly account for multiple acclimation processes and their distinct time-scales will help natural resource managers plan for ecological impacts of climate change from years to many decades into the future. We outline a synthetic research programme focused on the time-scales of ecological acclimation to reduce uncertainty in ecological forecasts.

climate adaptation↗

DOE OSTI2024

Digital Twinning and Predictive Modeling of Traffic for Safe, Efficient, and Reliable Intersections

Over the last decade, the advances in connected and autonomous vehicles (CAVs) have far surpassed the technological realm of transportation infrastructure. There is a growing need to have a technologically commensurate transportation infrastructure to enable safe and reliable movement of goods and people. The recent creation of Advanced Research Projects Agency - Infrastructure (ARPA-I) through the Infrastructure Investment and Jobs Act by the U.S. Department of Transportation (USDOT) has further amplified the need to revolutionize the transportation infrastructure system in the US. This need is perhaps felt most at traffic intersections, as more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. The proposed concept of Infrastructure Perception and Control (IPC) is aimed at bridging this technological gap by building a real-time digital twin of traffic by fusing detections from sensors installed at the intersection. This digital twin can then empower a wide variety of applications such as smart traffic signals or infrastructure-to-everything (I2X) communications. Smart signaling can help avoid crashes through early-prediction, while I2X can augment the CAV sensors under uncertain driving conditions and provide connected vehicles (CVs) with traffic information they can use to optimize their travel.

connected vehicles↗

DOE OSTI2023

Polarized fusion and potential in situ tests of fuel polarization survival in a tokamak plasma

Abstract The use of spin-polarized fusion fuels would provide a significant boost towards the ignition of a burning plasma. The cross section for D + T → α + n, would be increased by 1.5 if the fuels were injected with parallel polarization. Furthermore, our simulations demonstrate additional non-linear power gains in large-scale machines such as ITER, due to increased alpha heating. Such benefits require the survival of spin polarizations for periods comparable to the particle confinement time. During the 1980s, calculations predicted that polarizations could survive a plasma environment, although concerns persisted regarding the cumulative impacts of wall recycling. In that era, technical challenges prevented direct tests and left the large scale fueling of a power reactor beyond reach. Over the last decades, this situation has changed dramatically. Detailed simulations of ITER have predicted negligible wall recycling in a high-power reactor, and recent advances in laser-driven sources project the capability of producing large quantities of ∼100% polarized D and T. The remaining crucial step is an in-situ demonstration of polarization survival in a plasma. For this, we outline a measurement strategy using the isospin-mirror reaction, D + 3 He → α + p. Polarized 3 He avoids the complexities of handling tritium, while encompassing the same spin-physics. We evaluate two methods of delivering deuterium, using dynamically polarized Lithium-Deuteride (with vector polarization P V D of 70%) or frozen-spin Hydrogen-Deuteride (with P V D of 40%), together with a method of injecting optically-pumped 3 He (with 65% polarization). Pellets of these materials all have long polarization decay times (∼6 min for LiD at 2 K, ∼2 months for HD at 2 K, and ∼3 d for 3 He at 77 K), all far greater than a plasma shot in a research tokamak such as DIII-D (∼20 s). Both species can be propelled from a single cryogenic injection gun. We review plasma requirements and strategies for detecting polarization survival. Polarization alters both fusion yields and the angular distribution of fusion products, and each of these provides a potential signal. In this paper we simulate a selection of shots with similar characteristics in a future high-T ion H plasma, and find ratios of yields from shots with fuel spins parallel and antiparallel reaching 1.3 (HD + 3 He) to 1.6 (LiD + 3 He) over a wide range of poloidal angles. (A companion paper finds sensitivity to fusion product angular distributions as reflected in the pitch angles of protons and alphas reaching the plasma facing wall.)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Towards a robust out-of-the-box neural network model for genomic data

The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and targeted marketing given their prediction accuracy and their robust performance under big data settings. Yet neural network models have not made a successful transition into the medical and biological world due to the ubiquitous characteristics of biological data such as modest sample sizes, sparsity, and extreme heterogeneity. Here, we investigate the robustness, generalization potential and prediction accuracy of widely used convolutional neural network and natural language processing models with a variety of heterogeneous genomic datasets. Mainly, recurrent neural network models outperform convolutional neural network models in terms of prediction accuracy, overfitting and transferability across the datasets under study. While the perspective of a robust out-of-the-box neural network model is out of reach, we identify certain model characteristics that translate well across datasets and could serve as a baseline model for translational researchers.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next

Chemical kinetic modeling plays a foundational role in fields ranging from energy to environmental science, pharmaceuticals, and advanced materials. The past two decades have seen remarkable progress, particularly in modeling gas-phase reactions for thermochemical processes, leading to impactful industrial applications such as steam cracking and air quality management. However, new challenges are emerging. The successful development of systematic methodologies for the description of gas-phase kinetics opens the possibility to apply the same approach to the study of more challenging systems. Here, we review recent advances, including ab initio transition state theory-based master equation estimation of elementary rates, automated mechanism generation, machine-learning-assisted kinetics, and uncertainty quantification, and discuss the advances needed to apply the same methodological approach in areas such as heterogeneous catalysis, electrochemistry, liquid-phase and solid-state reactivity, and multiscale model integration. We advocate for the development of targeted tools, especially methods that go beyond empirical tuning toward first-principles-based predictions. We highlight the need for accessible software and AIaugmented workflows to democratize modeling for industry and academia alike. In this perspective, we call attention to not only what has worked but also what remains unsolved, advocating to avoid overemphasizing successes in scientific works at the expense of realism. The next decade should focus on predictive capability, physical accuracy, and community infrastructure (e.g., databases and services) to enable innovation across diverse fields. We argue that kinetic modeling, properly equipped, can accelerate discovery far beyond its traditional domains.

ab initio calculations↗

DOE OSTI2024

Sea Ice and Cloud Processes Mediating Compensation between Atmospheric and Oceanic Meridional Heat Transports across the CMIP6 Preindustrial Control Experiment

Abstract Bjerknes compensation (BJC) refers to the anticorrelation observed between atmospheric and oceanic heat transport (AHT/OHT) variability, particularly on decadal to longer time scales that may be important to the predictability of the climate system. This study investigates the spread in BJC across fully coupled simulations of phase 6 of the Coupled Model Intercomparison Project (CMIP6) and critical processes (particularly related to sea ice and clouds) that may contribute to that spread. BJC on decadal to longer time scales is confirmed across all the simulations evaluated, and it is strongest in the Northern Hemisphere (NH) between 60° and 70°N. At these latitudes, BJC appears to be primarily driven by the exchange of turbulent fluxes (sensible and latent) in the Greenland, Iceland, and Barents Seas. Metrics to break down how sea ice and clouds uniquely modify the radiative balance of the polar atmosphere during anomalous OHT events are presented. These metrics quantify the impacts of sea ice and clouds on surface and top of atmosphere (latent, sensible, longwave, and shortwave radiative) energy fluxes. Cloud responses tend to counter the clear sky impacts over the Marginal Ice Zone (MIZ). It is further shown that the degree of BJC present in a simulation at high latitudes is heavily influenced by the sensitivity of the sea ice to OHT, which is most influential over the MIZ. These results are qualitatively robust across models and explain the intermodel spread in NH BJC in the preindustrial control experiment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Developing Ideal Metalorganic Hydrides for Hydrogen Storage: from Theoretical Prediction to Rational Fabrication

Materials for hydrogen storage have been extensively explored for a few decades. Thousands of materials have been synthesized and tested, however, sparse systems could meet the practical requirements. Metalorganic hydrides discovered recently offer new opportunities. It is, however, extremely time-consuming and inefficient to experimentally screen potential materials from a large variety of metal cations and organic anions. In the present study, we performed wideranging theoretical predictions and screened more than 90 metalorganic hydrides, 20 of them were identified with both high hydrogen capacities (=5 wt%) and suitable thermodynamics (heat of H2 desorption: 25 ~ 35 kJ/mol-H2) that allows hydrogen uptake and release near ambient condition.

Jing, Zijun↗