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DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗

DOE OSTI2026

Atomic- and Molecular-Scale Interphase Engineering for High-Performance Solid-State Batteries

Solid-state batteries (SSBs) promise a decisive advance beyond conventional Li-ion systems, yet their development remains constrained by persistent solid–solid interfacial instabilities that degrade performance and durability. Interfaces between solid electrolytes and both cathodes and Li metal often exhibit poor wettability, limited physical contact, and high charge–transfer resistance, leading to chemical decomposition, mechanical failure, and impedance growth. Overcoming these limitations requires interphase engineering with atomic-scale precision—capabilities that conventional coating methods cannot reliably deliver. Atomic layer deposition (ALD) and molecular layer deposition (MLD) uniquely meet this need by enabling ultrathin, conformal, and composition-tunable films that stabilize reactive surfaces, suppress parasitic reactions, and regulate Li-metal morphology. Importantly, this Perspective highlights ALD/MLD systems that have already demonstrated effectiveness in liquid-electrolyte cells and discusses how these validated strategies can be deliberately translated to solid-state architectures. By grounding future directions in experimentally proven concepts rather than speculative hypotheses, we outline how atomic- and molecular-scale design principles can accelerate the development of robust, high-performance SSB technologies.

atomic and molecular layer deposition↗

DOE OSTI2020

Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows with AI and HPC

ynchrotron light sources are routinely used to perform imaging experiments. In this paper, we review the relevant computational stages, identify bottlenecks, and highlight future opportunities to streamline data acquisition for experimental microscopy workflows. We demonstrate our preliminary exploration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.

Mcclure, James↗

DOE OSTI2026

A generative machine learning model for designing metal hydrides applied to hydrogen storage

Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.

generative model↗

DOE OSTI2022

Accelerated radiation tolerance testing of Ti-based MAX phases

MAX phases have recently attracted significant attention for potential nuclear applications due to their novel properties such as unique hexagonal-compact nanolayered crystal structure, high-machinability due to lower hardness levels compared to conventional ceramics, and high-chemical inertness. In order for MAX phases to be used in nuclear reactors, two aspects deserve detailed investigations: (i) their phase stability at high-temperatures and (ii) microstructural defect formation and recovery induced by energetic particle irradiation. To date, degradation mechanisms of MAX phases at high-temperatures and following irradiation are largely unexplored fields of research. This work focuses on the evaluation of two Ti-based MAX phases – Ti 2 AlC and Ti 3 SiC 2 – within the context of extreme environments. To accomplish this, a one-of-a-kind comparison between neutron irradiations, performed over a decade of research at the High Flux Isotope Reactor (HFIR), and heavy-ion irradiations, carried out in situ in a Transmission Electron Microscope (TEM) at the MIAMI-2 facility, has been conducted. The results show Ti-based MAX phases are prone to accelerated decomposition under the conditions investigated. This questions the hypothesis that MAX phases exhibit high phase stability, especially when used in future nuclear energy systems where energetic particle irradiation is a dominating degradation mechanism.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Abstract The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Recent advances in chemical recycling and upcycling of plastic waste into valuable materials, chemicals, and energy: a comprehensive review

The global plastic waste crisis has increased in severity in recent years: annual plastic production is projected to reach 500 million metric tons by 2025, and plastic waste accumulation is expected to surpass 12 billion metric tons. Despite these growing volumes, only ∼9% of plastic waste is currently recycled; the majority is either landfilled, incinerated, or mismanaged, contributing to escalating greenhouse gas emissions—from 1.7 Gt carbon dioxide equivalent (CO 2 -eq.) in 2015 to an estimated 6.5 Gt CO 2 -eq. by 2050—and physical environmental pollution. This review provides a comprehensive overview of advanced plastic upcycling strategies to address this issue and recover value from diverse plastic waste streams. Recent developments in solvent-based dissolution, chemical depolymerization, and thermochemical conversions are examined for major plastic types, including polyolefins, polycondensation polymers, and PVC. Underlying reaction pathways, catalyst designs, and processing parameters that govern product selectivity, efficiency, and conversion yields are discussed in depth. Emerging techniques such as microwave-assisted depolymerization, tandem catalysis, and co-processing approaches are highlighted for their potential to enhance efficiency under milder conditions. Emphasis is also placed on the production of high-value products such as monomers, naphtha-range hydrocarbons, and syngas, and discussion is provided on catalyst stability, contaminant removal, scalability, life cycle effects on the environment, and technoeconomic viability. Finally, the review outlines future research directions focused on catalyst innovation, integrated process design, supportive policy frameworks, and interdisciplinary collaboration. All recommendations are aimed at accelerating large-scale implementation of plastic upcycling technologies and advancing the global circular plastics economy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Single-cell and spatial omics in plants: from cellular atlases to regulatory mechanisms

Single-cell RNA sequencing (scRNA-seq) has transformed transcriptomic studies by enabling gene expression profiling at the resolution of individual cells within and across a broad range of tissue types, revealing cellular heterogeneity that is obscured in bulk tissue transcriptomes. Over the past decade, improvements in microfluidics and library preparation have drastically increased throughput, allowing tens of thousands of cells to be assayed in a single experiment. Although initially developed in animal systems, scRNA-seq has rapidly emerged as a powerful and widely adopted approach in plant biology. Beyond transcriptomics, the integration of single-cell data with chromatin accessibility, proteomics, metabolomics, and spatial omics is enabling a system-level understanding of plant gene regulation and cellular organization. Network-based analytical frameworks further support the reconstruction of gene regulatory networks and the interpretation of complex single-cell data. In this review, we summarize the current technological landscape of plant single-cell studies, discuss key experimental and analytical challenges, and review emerging strategies for validating single-cell discoveries. We also discuss future directions in applying single-cell technologies to woody perennials plants and bioenergy-relevant crops, emphasizing their potential to accelerate the discovery of cell type-specific regulatory mechanisms underlying growth, stress resilience, and biomass production.

Li, Miaomiao [ORNL] (ORCID:0000000321326168)↗

DOE OSTI2016

Probing short-range nucleon-nucleon interactions with an electron-ion collider

For this research, we derive the cross section for exclusive vector meson production in high-energy deeply inelastic scattering off a deuteron target that disintegrates into a proton and a neutron carrying large relative momentum in the final state. This cross section can be expressed in terms of a novel gluon transition generalized parton distribution (T-GPD); the hard scale in the final state makes the T-GPD sensitive to the short-distance nucleon-nucleon interaction. We perform a toy model computation of this process in a perturbative framework and discuss the time scales that allow the separation of initial- and final-state dynamics in the T-GPD. We outline the more general computation based on the factorization suggested by the toy computation: In particular, we discuss the relative role of “pointlike” and “geometric” Fock configurations that control the parton dynamics of short-range nucleon-nucleon scattering. With the aid of exclusive J/ψ production data at the Hadron-Electron Ring Accelerator at DESY, as well as elastic nucleon-nucleon cross sections, we estimate rates for exclusive deuteron photodisintegration at a future Electron-Ion Collider (EIC). Our results, obtained using conservative estimates of EIC integrated luminosities, suggest that center-of-mass energies sNN ~12GeV 2 of the neutron-proton subsystem can be accessed. We argue that the high energies of the EIC can address outstanding dynamical questions regarding the short-range quark-gluon structure of nuclear forces by providing clean gluon probes of such “knockout” exclusive reactions in light and heavy nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Risk aware net load balancing in micro grids with high DER penetration

The rapid transformation of micro grids due to the accelerated integration of renewables, storage systems and IoT enabled monitoring and control has opened up new opportunities as well as challenges. Future micro grids will be characterized by high DER penetration and will require sophisticated net load balancing frameworks which explicitly consider the errors in prediction of load and generation due to uncertainty in weather conditions while making decisions. Traditional techniques for grid net load management which rely on isolated shaping of load and supply curves are inadequate and inefficient. For micro grids with high PV penetration, the intermittent and unpredictable nature of PV based energy generation can lead to dramatic and sudden supply demand imbalances thus requiring a holistic framework for balancing net load over the entire horizon. In this paper, we develop a sequential decision making framework for net load management that optimally balances the usage of storage and energy market transactions as a mechanism for mitigating supply demand imbalances (net load imbalances) over the horizon. Our framework specifically accounts for prediction uncertainty of future net load imbalances and minimizes the tail end risk of storage shortfall at the end of the horizon. Using qualitative analysis, we show that our framework achieves its objective of minimum cost net load balancing while accounting for the tail end risk.

14 SOLAR ENERGY↗

DOE OSTI2023

Exascale Was Not Inevitable; Neither Is What Comes Next

The long, steady advance of supercomputing capabilities historically makes it tempting to assume that similar future improvements are inevitable. In fact, the successful path to exascale systems was far from certain, requiring a shift to accelerator-based designs, an associated end-to-end rethinking of traditional computational approaches, and a fundamental change in how scientists collaborate and interact. Here in this article, we share lessons from the U.S. Department of Energy Exascale Computing Project along with perspectives on how to design and implement scientific applications in the exascale and post-exascale eras.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Design and Analysis of Muon Beam Stop Support Structures [Thesis]

The primary objective of this thesis is to design and analyze support structures to be used in the installation, test and final positioning of the MBS throughout the life of the Mu2e experiment. There several requirements for the MBS imposed by both the scope of the experiment and, other components within the DS bore. The functions of the MBS are: 1. To limit the induced rates in the Tracker, the Calorimeter and the Cosmic Ray Veto due to backsplash-and-secondary interactions, and 2. To reduce radiation levels external to the Detector solenoid. The structures used in supporting the MBS will also adhere to requirements imposed by its functions. These requirements are critical to the support structures and affect design decisions. Other requirements critical to the design are imposed by the weight, positional tolerance and assembly procedure of the MBS, and also, the magnetic field and vacuum dose rate of the DS bore. A detailed breakdown of how each requirement affects the structural design can be found in chapter 2. Chapter 3 describes the design of each support structure and its attachment to the MBS while chapter 4 describes the results from structural analysis of the support structures. Chapter 5 describes evaluation for the design through testing and calculations while the conclusion in chapter 6 reports the current status at the time of this thesis submission with a plan for future work to be completed until final design and installation.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Fuel/Basket Degradation Modeling Summary

This report documents the activities in a preliminary phase of development for three models: 1) waste package breach model, 2) fuel/basket degradation model, and 3) dual-purpose canister (DPC) crush model. The waste package breach model describes the coupling of mass flow, heat transport, and canister shell deformation in response to a heat-generating (criticality) event. The fuel/basket degradation model describes potential weakening and disaggregation of the DPC structure from corrosion, possibly accelerated by seismic ground motion. Progressively degraded three-dimensional (3D) configurations of the fuel, basket, and shell are generated for future analysis of reactivity (with as-loaded DPC fuel characteristics). Another important application for the fuel/basket degradation model is validation of the two stylized degradation cases currently being used by other investigators for analysis of the as-loaded DPC inventory under disposal conditions. The DPC crush model investigates stability of a typical DPC after breach of the disposal overpack allows fluids from the repository near-field environment to penetrate and externally pressurize the canister shell. Preliminary results show that large deformation of the DPC could occur for external pressure on the order of 10 to 15 MPa, or the shell could be stable (not collapse) with pressure of 20 MPa or greater if the basket plates are fully welded at the connections.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Software Contribution to the L-CAPE project

The L-CAPE project utilizes asynchronous data from thousands of Linear Accelerator devices and applies data science techniques to detect anomaly of accelerator failure before the incident, as well as automatic labels of accelerator outages. The author describes her contribution progress for the L-CAPE project this summer, as well as suggestions for future interns working on the project.

Tang, Jasmine↗

DOE OSTI2016

Evolution of the proton velocity distribution due to stochastic heating in the near-sun solar wind

We investigate how the proton distribution function evolves when the protons undergo stochastic heating by strong, low-frequency, Alfvén-wave turbulence under the assumption that β is small. We apply our analysis to protons undergoing stochastic heating in the supersonic fast solar wind and obtain proton distributions at heliocentric distances ranging from 4 to 30 solar radii. We find that the proton distribution develops non-Gaussian structure with a flat core and steep tail. For r>5 R{sub S}, the proton distribution is well approximated by a modified Moyal distribution. Comparisons with future measurements from Solar Probe Plus could be used to test whether stochastic heating is occurring in the solar-wind acceleration region.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2015

Design and Testing of a Small Inductive Pulsed Plasma Thruster

The design and testing of a small inductive pulsed plasma thruster (IPPT), shown in Fig. 1 with all the major subsystems required for a thruster of this kind are described. Thrust measurements and imaging of the device operated in rep-rated mode are presented to quantify the performance envelope of the device. The small IPPT described in this paper was designed to serve as a test-bed for the pulsed gas-valves and solid-state switches required for a IPPTs. A modular design approach was used to permit future modifications and upgrades. The thruster consists of the following sub-systems: a) a multi-turn, spiral-wound acceleration coil (27 cm o.d., 10 cm i.d.) driven by a 10 microFarad capacitor and switched with a high-voltage thyristor, b) a fast pulsed gas-valve, and c.) a glow-discharge pre-ionizer (PI) circuit. The acceleration-coil circuit may be operated at voltages up to 4 kV (the thyristor limit is 4.5 kV). The device may be operated at rep-rates up to 30 Hz with the present gas-valve. Thrust measurements and imaging of the device operated in rep-rated mode will be presented. The pre-ionizer consists of a 0.3 microFarad capacitor charged to 4 kV and connected to two annular stainless-steel electrodes bounding the area of the coil-face. The 4 kV potential is held across them and when the gas is puffed in over the coil, the PI circuit is completed, and a plasma is formed. Even at the less than optimal base-pressure in the chamber (approximately 5 × 10(exp −4) torr), the PI held-off the applied voltage, and only discharged upon command. For a capacitor charge of 2 kV the peak coil current is 4.1 kA, and during this pulse a very bright discharge (much brighter than from the PI alone) was observed (see Fig. 2). Interestingly, for discharges at this charge voltage the PI was not required as the current rise rate, dI/dt, of the coil itself was sufficient to ionize the gas.

Martin, Adam K.↗

NASA NTRS2016

Scientific Considerations for Future Spectroscopic Measurements from Space of Activity on the Sun

High-resolution UV and X-ray spectroscopy are important to understanding the origin and evolution of magnetic energy release in the solar atmosphere, as well as the subsequent evolution of heated plasma and accelerated particles. Electromagnetic radiation is observed from plasma heated to temperatures ranging from about 10 k K to above 10 MK, from accelerated electrons emitting photons primarily at X-ray energies, and from ions emitting in gamma rays. These observations require space-based instruments sensitive to emissions at wavelengths shorter than the near UV. This article reviews some recent observations with emphasis on solar eruptive events, the models that describe them, and the measurements they indicate are needed for substantial progress in the future. Specific examples are discussed demonstrating that imaging spectroscopy with a cadence of seconds or better is needed to follow, understand, and predict the evolution of solar activity. Critical to substantial progress is the combination of a judicious choice of UV, EUV, and soft X-ray imaging spectroscopy sensitive to the evolution of this thermal plasma combined with hard X-ray imaging spectroscopy sensitive to suprathermal electrons. The major challenge will be to conceive instruments that, within the bounds of possible technologies and funding, have the flexibility and field of view to obtain spectroscopic observations where and when events occur while providing an optimum balance of dynamic range, spectral resolution and range, and spatial resolution.

Holman, Gordon D.↗

NASA NTRS2020

Vibration-based health characterization of multiple IMUs in UAV applications

Inertial measurement units (IMUs) are vital in UAV navigation for vehicle attitude and position estimation, especially in the frequently-occurring case of temporary loss of GPS capabilities. Degradation in these units could yield inaccurate position estimates, resulting in incorrect input for vehicle control systems. A wide variety of these units exist to meet the needs of UAV operators, general aviation, and military vehicles. In recent decades, lightweight and inexpensive MEMS sensors have grown in popularity with respect to UAVs. Studies into the failure modes of MEMS devices suggest that vibration is one of the leading causes of MEMS degradation. MEMS sensors are generally encased in their own hermetic packaging, as their small size and sensitivity cause them to be particularly vulnerable to air damping and particulate contamination when exposed. Exposure to excessive vibration can cause cracking in this packaging as well as damage to connecting electronics, with recorded results that cause sensors to act outside of their specifications, potentially providing unreliable input to controllers. A prior study from the Politecnico di Torino looked into the degradation of MEMS IMUs, specifically the AXIS-AIS402, for aerospace and vibrating environments. In the study, the authors found that after exposing MEMS IMUs to simulated vibrations from working conditions of aerospace applications, the sensors showed increased noise and increased bias instability. At NASA Ames, several widely-used MEMS IMUs were selected for a new IMU degradation study, comparing accelerometer performance before and after exposure to similar levels of vibration simulating aerospace conditions. The IMUs were selected from the Pixhawk autopilot systems, hobby sUAS, or prior experimental studies at NASA. Additionally, the industrial grade VectorNav-100 IMU was selected as a ground truth reference point to which the performance of the test sensors can be compared. In the study, each sensor is exposed to functional levels of UAV vibration as replicated on a single-frequency vibratory table in the NASA Ames SHARP Laboratory, and each accelerometer is characterized for bias, drift, and noise characteristics both before and after vibratory exposure. The focus of this study is to monitor the health of the IMU sensors and determine if sensor degradation can be detected from output error and frequency spectrum analysis, rather than from microscopic examination of the sensors. In this case, sensor degradation and faulty data can be more easily detectable by non-experts hoping to use these sensors as an important piece of an integrated package. In addition, specific sensor degradation modes potentially leading to in-flight hazards could be identified, such as accelerated levels of drift and altered temperature response. The overall goal of this work is to increase levels of safety for future users of these sensors and to provide a clear analysis of sensor limitations currently lacking in most sensor specification documentation.

IMUs↗