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DOE OSTI2022

labquake_future_prediction

The labquake_future_prediction code is a collection of python modules and scripts that serves as supporting information for the article “Predicting future laboratory fault friction through deep learning” for publication in the journal of “Geophysical Research Letters”. It is designed to predict laboratory fault slips in the immediate future by scanning continuous acoustic emission (AE) waveforms recorded in laboratory biaxial shear experiments. The predictions are made with a deep learning model based on convolutional encoder-decoder (CED) models and the Transformer model primarily developed for Natural Language Processing (NLP). The deep learning model is trained with the tensorflow package using publicly available laboratory data sets in standard binary file format in numpy. The utility functions for reading data files, configuring model hyperparameters, constructing the CED and Transformer models, training and testing of the models are defined in python module files. The workflow of training the models for labquake future predictions and the multiple GPU’s rapid model hyperparameter optimization as described in the journal article, are demonstrated in accompanying python script files and Jupyter notebooks.

Wang, Kun↗

NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗

DOE OSTI2025

Towards large-scale quantum optimization solvers with few qubits

Quantum computers hold the promise of more efficient combinatorial optimization solvers, which could be game-changing for a broad range of applications. However, a bottleneck for materializing such advantages is that, in order to challenge classical algorithms in practice, mainstream approaches require a number of qubits prohibitively large for near-term hardware. Here we introduce a variational solver for MaxCut problems over $m={{\mathcal{O}}}({n}^{k})$ binary variables using only n qubits, with tunable k > 1. The number of parameters and circuit depth display mild linear and sublinear scalings in m , respectively. Moreover, we analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature. Altogether, this leads to high quantum-solver performances. For instance, for m = 7000, numerical simulations produce solutions competitive in quality with state-of-the-art classical solvers. In turn, for m = 2000, experiments with n = 17 trapped-ion qubits feature MaxCut approximation ratios estimated to be beyond the hardness threshold 0.941. Our findings offer an interesting heuristics for quantum-inspired solvers as well as a promising route towards solving commercially-relevant problems on near-term quantum devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Sequential Deposition of Multicomponent Bulk Heterojunctions Increases Efficiency of Organic Solar Cells

Constructing tandem and multi-blend organic solar cells (OSCs) is an effective way to overcome the absorption limitations of conventional single-junction devices. However, these methods inevitably require tedious multilayer deposition or complicated morphology-optimization procedures. Herein, sequential deposition is utilized as an effective and simple method to fabricate multicomponent OSCs with a double-bulk heterojunction (BHJ) structure of the active layer to further improve photovoltaic performance. Two efficient donor-acceptor pairs, D18-Cl:BTP-eC9 and PM6:L8-BO, are sequentially deposited to form the D18-Cl:BTP-eC9/PM6:L8-BO double-BHJ active layer. In these double-BHJ OSCs, light absorption is significantly improved, and optimal morphology is also retained without requiring a more complicated morphology optimization involved in quaternary blends. Compared to the quaternary blend devices, energy loss (E loss ) is also reduced by rationally matching each donor with an appropriate acceptor. Consequently, the power conversion efficiency (PCE) is improved from 18.25% for D18-Cl:BTP-eC9 and 18.69% for PM6:L8-BO based binary blend OSCs to 19.61% for the double-BHJ OSCs. In contrast, a D18-Cl:PM6:L8-BO:BTP-eC9 quaternary blend of OSCs exhibited a dramatically reduced PCE of 15.83%. Finally, these results demonstrate that a double-BHJ strategy, with a relatively simple processing procedure, can potentially enhance the device performance of OSCs and lead to more widespread use.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Enhancing Electron Microscopy Image Classification Using Data Augmentation

Manual labeling for machine learning tasks such as image classification is tedious and labor-intensive; as a result, scientific datasets suitable for deep learning applications are scarce and limited. While data augmentation techniques have shown promise for extending image datasets, very little work has been done to understand the impact of combining multiple augmentation methods sequentially or the limits of their effectiveness when combined. Our work addresses this gap by examining how standard and combinatorial data augmentation affects the performance of machine learning models when trained on small datasets for label classification tasks. For our analysis, we generate single, double and quadruple-augmented datasets for a microscopy image classification task using six standard augmentation methods, and compare the resultant improvements observed in binary classification accuracy with three standard image classification models (DenseNet169, MobileNetV2, ResNet101V2). Our experiments show a non-monotonic relationship between the number of simultaneous augmentation methods and classification accuracy, indicating that there is a trade-off between the degree of augmentation and the model performance. These findings suggest that the optimal number of augmentation methods will vary by domain and use case. We also find that the order in which augmentation methods are applied to a limited dataset matters when combining augmentation schemes, with our use case showing performance differences up to 2.6% when the augmentation order is reversed for double-augmented datasets. Our work offers insights to the limits of data augmentation when working on image classification tasks with limited datasets.

Welsman, Jordan A↗

DOE OSTI2024

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO 2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO 2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO 2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO 2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Geochemistry & Geophysics↗

DOE OSTI2020

Single-phase Ru 1- x-y Mn x Co y O 2 nanoparticles as highly effective oxygen reduction electrocatalysts in alkaline media with enhanced stability and fuel-tolerance

Here, a series of single-phase binary and ternary ruthenium and transition metal oxide nanoparticles supported on Vulcan - Ru 1-x MnxO2/C, Ru1-xCoxO 2 /C, Ru 1-x-y MnxCo y O 2 /C, Ru 1-x Fe x O 2 /C, Ru 1-x Ni x O 2 /C and Ru 1-x V x O 2 /C, as well as RuO 2 /C, MnO 2 /C, Co 3 O 4 /C and Mn 3-x Co x O 4 /C - were synthesized via a facile method and studied for their oxygen reduction reaction (ORR) activity in alkaline media. Single-phase Ru 1-x Mn x O 2 /C, Ru 1-x-y Mn x Co y O 2 /C and Ru 1-x Co x O 2 /C catalysts significantly boosted ORR kinetics in alkaline media. Moreover, they were H 2 and methanol tolerant, and exhibited long term stability due to their single-phase structure. Ru 0.85 Mn 0.15 O 2 /C stood out as the most active catalyst among them. A volcano relationship of the ORR activity of these binary and ternary metal oxide catalysts vs. the O adsorption energy was found, with the maximum activity observed for the Ru 1-x Mn x O 2 /C, Ru 1-x-y Mn x Co y O 2 /C and Ru 1-x Co x O 2 /C catalysts due to their optimal O binding energy. These materials have potential applications as highly active ORR catalysts in alkaline fuel cells and metal-air batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format

Advances in mass spectrometry (MS) instrumentation, such as higher resolution, faster scan speeds, and improved sensitivity, have significantly increased the volume and complexity of data. The growing adoption of imaging and ion mobility further amplifies these challenges across MS-based omics fields, including proteomics, metabolomics, and lipidomics. While these technologies unlock new possibilities, they also present significant challenges in data management, storage, and accessibility. Existing open formats, such as the XML-based community standards mzML and imzML, struggle to meet the demands of modern MS workflows due to their large file sizes, slow data access, and limited metadata support. Vendor-specific formats, while optimized for proprietary instruments, lack interoperability, comprehensive metadata support and long-term archival reliability. This white paper lays the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multi-dimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will enable enhanced interoperability across platforms, seamless support for complex workflows including ion mobility and MS imaging, and faster data access compared to existing community formats such as mzML. Its design will ensure data is managed in compliance with regulatory standards, essential for applications such as precision medicine and chemical safety, where long-term data integrity and accessibility are critical. For vendors, mzPeak provides a streamlined, open alternative to proprietary formats, reducing the burden of regulatory compliance while aligning with the industry's push for transparency and standardization. By offering a high-performance, interoperable solution, mzPeak positions vendors to meet customer demands for sustainable data management tools which will be able to handle emerging and future data types and workflows. mzPeak aspires to become the cornerstone of MS data management, empowering researchers, vendors, and developers to innovate and collaborate more effectively.

data formats↗

DOE OSTI2021

Exploring the physics of cesium lead halide perovskite quantum dots via Bayesian inference of the photoluminescence spectra in automated experiment

The unique optoelectronic properties of metal halide perovskite quantum dots (QDs) make them promising candidates for applications in light-emitting diodes (LEDs), scintillators, and other photonic devices. The automated micropipetting synthesis platform equipped with an optical reader enables the opportunity for high throughput synthesis and photoluminescent (PL) characterization of metal halide perovskite QDs for the first time. Here, we explore the compositional dependence of the PL behavior and stability of the combinatorial library of cesium lead halide (CsPbX 3 ) perovskites QDs via the automated platform. To study the stability of synthesized QDs in the binary and ternary configurations, we study the time-dependent PL properties using previously developed machine learning analysis. Additionally, to systematically explore the PL behavior in the ternary CsPbX 3 QDs system, we introduce the Bayesian inference framework that allows the probabilistic fit of multiple models to the PL data and establishes both optimal model and model parameter robustly. Furthermore, these behaviors can be used as a control parameter for the navigation of the multidimensional compositional spaces in automated synthesis. Furthermore, this analysis shows the nonuniformity of the PL peak behavior in the ternary CsPbX 3 QDs system. Further, the analysis confirms narrow size distribution and good quality of CsPbBr 3 QDs alloyed with low concentrations of iodide and chloride. We note that Bayesian Inference fit parameters can be further used as a control signal for navigation of the chemical spaces in automated synthesis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss

Training a neural network for picking seismic phase arrivals has been commonly posed as a segmentation problem. It is a highly imbalanced segmentation problem in the sense that the background vastly dominates the foreground because we are trying to pick the optimal single sample point that represents the arrival of a seismic phase in a many seconds long time window. Here, we test the Dice loss, which is a preferred loss function for highly imbalanced image segmentation problems. We show that phase-picking neural networks trained on the Dice loss behave in a binary fashion for which the prediction output is almost always either nearly 1 or nearly 0. This feature removes the strong dependence of data processing workflows on the prediction score threshold, which is an otherwise critical parameter to determine when using neural networks trained on the cross-entropy loss. When strategically used, models trained on the Dice loss can reduce the parameter dependency of machine learning-based seismic monitoring.

58 GEOSCIENCES↗

DOE OSTI2025

A Novel Gene Stacking Method in Plant Transformation Utilizing Split Selectable Markers

Gene stacking, the process of introducing multiple genes into a single plant to enhance desired traits, is essential for plant genetic improvement through both conventional breeding and genetic transformation. In general, transformation-based gene stacking can be achieved through either co-transformation to simultaneously introduce multiple genes or sequential multi-round transformation. While co-transformation is generally faster and more efficient than sequential multi-round transformation, it often requires two selectable marker genes, which confer resistance to antibiotics, for selecting transgenic events. However, in most cases, there is only one best selectable marker gene for a specific plant species or genotype. Also, it is harder to optimize the concentrations of two antibiotics for co-transformation than using one antibiotic for selecting transgenic events. To overcome this challenge, we recently developed an innovative split selectable marker system for plant co-transformation, allowing the use of one selectable marker gene to select transgenic events. This method involves constructing two binary vectors, each carrying a subset of genes of interest and a partial fragment of the selectable marker gene, which is connected to a partial intein fragment. Following Agrobacterium -mediated co-transformation, plants harboring both binary vectors are selected using a single antibiotic, such as kanamycin. This split-marker system can be used to co-transform multiple genes into both herbaceous and woody plants, accelerating genetic improvement of polygenic traits or integrative improvement of multiple traits to simultaneously increase crop yield and quality.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1993

Asymmetric three-beam binary optic grating

Binary and diffractive optical components are finding many applications in optical systems and integrated optical devices. A recent application required the development of a two-dimensional diffraction grating to perform an asymmetric, three-beam fanout. In this paper, techniques are presented for the design of arbitrary fanout grating devices. Modeling and optimization processes are demonstrated for the three-beam grating. The counter-intuitive results of the initial design are discussed, and experimental data verifying performance are presented.

Kathman, Alan D.↗

NASA NTRS1996

The First Diffraction-Limited Images from the W. M. Keck Telescope

The first diffraction limited, 0.05s resolution, images on the W. M. Keck Telescope have been obtained at a wavelength of 2.2 micrometers. These images were part of an experiment to test the suitability of the Keck Telescope for speckle imaging. In order to conduct this test, it was necessary to modify the pixel scale of the Keck facility Near Infrared Camera (NIRC) to optimally sample the spatial frequencies made available by the Keck telescope. The design and implementation of the external reimaging optics, which convert the standard fl25 beam from the secondary mirror to fl182, are described here. Techniques for reducing speckle data with field rotation on an alt-az telescope are also described. Three binary stars were observed in this experiment with separations as small as 0.05s. With only 100 frames of data on each, a dynamic range of at least 3.5 mag was achieved in all cases. These observations imply that a companion as faint as 14.5 mag at 2.2 micrometers could be detected around an 11th magnitude point source.

Matthews, K.↗

DOE OSTI2026

Synergistic CuWO 4 /NiS 2 Binary Nanostructures for Efficient Photocatalytic Hydrogen Production

A viable and ecologically safe method for producing green hydrogen is photocatalytic hydrogen (H 2 ) production. However, the development of effective semiconductor materials with improved activity and long-term stability is still a major obstacle. Here, in this work, we report the development of a broadband-gap, UV light-responsive CuWO 4 /NiS 2 nanocomposite using simple hydrothermal and wet impregnation techniques. The hybrid systems, with photocatalytic presentation, were thoroughly assessed using spectroscopic, photophysical, and microscopic characterization methods. The optimized CuWO 4 /NiS 2 nanostructure demonstrated an impressive H 2 generation rate of 28.2 mmol h –1 g –1 under light irradiation, which is roughly 2.71 and 3.28 times greater than those of pristine CuWO 4 (10.4) and NiS 2 (8.6 mmol h -1 g (cat) -1 ), respectively. The enhanced presentation is ascribed to the synergetic interface between the coupled semiconductors, which facilitates the formation of a nanostructure. This arrangement inhibits electron–hole recombination and encourages effective charge carrier parting. Additionally, compared with pristine CuWO 4 and NiS 2 , respectively, the CuWO 4 /NiS 2 composite showed a noticeably greater photocurrent density. The developed nanostructured CuWO 4 /NiS 2 displayed a 28.6% quantum efficiency at 450 nm. These outcomes validate CuWO 4 /NiS 2 ’s significant aptitude as a non-noble-metal photocatalyst for the production of sustainable hydrogen.

CuWO4/NiS2↗

DOE OSTI2020

GIS-Based Modeling of Contaminated Soil Volumes at Multiple Sites in the Formerly Utilized Sites Remedial Action Program - 20149

The remediation of hazardous, toxic, and radioactive waste (HTRW) sites produces cost-related risks associated with the estimation of contaminated soil or debris volumes. Historical risk-management techniques include cost contingencies to cover volume uncertainties that affect project budgeting and decision-making. The Buffalo District teamed with project partners to lessen volume uncertainty and reduce project risks at multiple HTRW sites managed under the Formerly Utilized Sites Remedial Action Program (FUSRAP). Historical remedial investigations under FUSRAP commonly identified the presence of radiological material in site media, the associated human health risk, and then areas of remediation. To manage remedial execution and reduce risk, pre-design or remediation-phase sampling essentially 'chased' contamination, which was not conducive to efficient predictive budgeting derived from Feasibility Study (FS) cost analyses. The Buffalo District first optimized their approach to better understand volume uncertainty by utilizing the Argonne National Laboratory's Bayesian Approaches for Adaptive Spatial Sampling (BAASS) software [1]. BAASS processed soft data (e.g., gamma walk-over data) and spatial sampling data to estimate the lateral extent of contaminated soil irrespective of depth (i.e., gross contamination extent) and define areas of contaminant uncertainty. The software performed a binary transformation of contaminant concentrations at all sampling points based upon remedial action goals or a sum of ratios approach (i.e., clean, impacted, or range of impacts in soil). The model produced two-dimensional (horizontal) contaminant probability contours and statistical uncertainty in the sampling coverage and resulting contaminant extents. This method was translated vertically by partitioning the sampling data into depth brackets that produced a stacked representation of contaminant extents and uncertainty in the subsurface (i.e., similar to construction lifts). The results commonly led to a better understanding of project uncertainty and the need for sampling strategies that produce high-confidence soil volumes, which control costs. The BAASS-based delineations were eventually replaced by Empirical Bayesian Kriging (EBK) methods available in ArcGIS Spatial or 3D Analysts [2]. The EBK method calculates contaminant probability zones derived from user-controlled semivariograms of the spatial datasets. The resulting probability zones (e.g., 50% or 80% of contaminant probability) represent the two-dimensional surface delineation of the overall horizontal remedial area, similarly to BAASS. However, unlike BAASS, the vertical sampling data within these probability zones became vertical control points to contour a subterranean surface that connects subsurface points to the land-surface delineations of contamination. The resulting representation of horizontal and vertical impacts within an enclosed envelop (volume) of soil included uncertainty distributions that are used to plan uncertainty-reduction sampling. These data-driven and math-based models of three-dimensional sampling results produced well-bounded remedial volumes for project planning and better uncertainty predictions during project budgeting. The EBK method was applied to several FUSRAP sites managed by the Buffalo District and compared to less rigorously modeled sites previously remediated by the District. The comparison of modeled to actual remediated volumes provide a basis for validating the volume-estimation method. This comparison is important to ensure modeled volumes match physical boundaries of site remediation. FUSRAP sites with denser investigative sampling and lesser volume uncertainty proved useful in remedial planning and contracting. The Buffalo District noted that historical sites with sparser sampling arrays had greater disparity between estimated volumes and final remedial volumes. The benefit achieved over the cost of detailed soil sampling appears positive for FUSRAP projects, especially where impacts vary widely and appear unbounded by investigation-phase sampling. The subsequent Empirical Bayesian Kriging of contamination coupled with vertical contouring for soil estimations reduces uncertainty in soil volumes or indicates where sampling is required to reduce uncertainty, which together optimize remedial planning and budgeting. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Thermodynamic Modeling of Aqueous LiCl, LiBr, LiI, and LiNO 3 Solutions

Thermodynamic models are essential to facilitate the advancement of process design, optimization, and operation of electrolyte systems. In this work, a comprehensive thermodynamic framework based on the Electrolyte Nonrandom Two-Liquid model is developed to calculate phase equilibria behavior and salt solubility of aqueous LiCl, LiBr, LiI, and LiNO 3 solutions. The model describes the non-ideality of the electrolyte solutions by using two binary interaction parameters for each electrolyte-molecule pair in the system. Each binary interaction parameter is further expressed with up to three temperature coefficients which are regressed from experimental data. To take into account the hydration of lithium ion, two separate chemistries for the dissociation of lithium salts are investigated. In the first case, the lithium ion is considered as a bare ion, Li + , while in the second case hydration of the lithium ion from Li + to Li(H 2 O) + is considered. Here, the calculated thermodynamic properties compare adequately with the experimental data for both sets of chemistries for concentrations up to saturation and temperatures from 273.15 K up to 623.15 K. Moderate to significant improvements are observed with the incorporation of the hydration chemistry for aqueous LiCl, LiBr, and LiI solutions when compared to the non-hydrated lithium ion model results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Experimental investigation and thermodynamic modeling of the binary Pt–Sn system

Here, the thermodynamics of the Pt–Sn system is revisited with attention to phase equilibria involving PtSn 4 , a topological quantum material with interesting physical properties. The phase relationship in the Sn-rich region of the Pt–Sn binary system was investigated by a combination of differential scanning calorimetry and equilibration experiments. Based on the present experiments and a critical evaluation of the available thermodynamic and phase diagram data reported in the literature, thermodynamic assessments of the entire Pt–Sn system were performed using the CALPHAD approach. Two sets of thermodynamic parameters were optimized, where the liquid phase was described using the Bragg-Williams random mixing model and the associate solution model, respectively. The modeling results are compared with the reliable experimental data and are shown to provide good representations of the phase equilibria and thermodynamic properties in this system.

36 MATERIALS SCIENCE↗

DOE OSTI2020

New interaction potentials for borate glasses with mixed network formers

Here, we adapt and apply a recently developed optimization scheme used to obtain effective potentials for aluminosilicate glasses to include the network former boron into the interaction parameter set. As input data for the optimization, we used the radial distribution functions of the liquid at high temperature generated by ab initio molecular dynamics simulations, and density, coordination, and elastic modulus of glass at room temperature from experiments. The new interaction potentials are shown to reliably reproduce the structure, coordination, and mechanical properties over a wide range of compositions for binary alkali borates. Furthermore, the transferability of these new interaction parameters allows mixing to reliably reproduce the properties of various boroaluminate and borosilicate glasses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗