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DOE OSTI2021

Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities

Abstract Beamline experiments at central facilities are increasingly demanding of remote, high-throughput, and adaptive operation conditions. To accommodate such needs, new approaches must be developed that enable on-the-fly decision making for data intensive challenges. Reinforcement learning (RL) is a domain of AI that holds the potential to enable autonomous operations in a feedback loop between beamline experiments and trained agents. Here, we outline the advanced data acquisition and control software of the Bluesky suite, and demonstrate its functionality with a canonical RL problem: cartpole. We then extend these methods to efficient use of beamline resources by using RL to develop an optimal measurement strategy for samples with different scattering characteristics. The RL agents converge on the empirically optimal policy when under-constrained with time. When resource limited, the agents outperform a naive or sequential measurement strategy, often by a factor of 100%. We interface these methods directly with the data storage and provenance technologies at the National Synchrotron Light Source II, thus demonstrating the potential for RL to increase the scientific output of beamlines, and layout the framework for how to achieve this impact.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Sharp, high numerical aperture (NA), nanoimprinted bare pyramid probe for optical mapping

The ability to correlate optical hyperspectral mapping and high resolution topographic imaging is critically important to gain deep insight into the structure–function relationship of nanomaterial systems. Scanning near-field optical microscopy can achieve this goal, but at the cost of significant effort in probe fabrication and experimental expertise. To overcome these two limitations, we have developed a low-cost and high-throughput nanoimprinting technique to integrate a sharp pyramid structure on the end facet of a single-mode fiber that can be scanned with a simple tuning-fork technique. The nanoimprinted pyramid has two main features: (1) a large taper angle (~70°), which determines the far-field confinement at the tip, resulting in a spatial resolution of 275 nm, an effective numerical aperture of 1.06, and (2) a sharp apex with a radius of curvature of ~20 nm, which enables high resolution topographic imaging. Optical performance is demonstrated through evanescent field distribution mapping of a plasmonic nanogroove sample, followed by hyperspectral photoluminescence mapping of nanocrystals using a fiber-in-fiber-out light coupling mode. Through comparative photoluminescence mapping on 2D monolayers, we also show a threefold improvement in spatial resolution over chemically etched fibers. These results show that the bare nanoimprinted near-field probes provide simple access to spectromicroscopy correlated with high resolution topographic mapping and have the potential to advance reproducible fiber-tip-based scanning near-field microscopy.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Electronic Bottleneck Suppression in Next‐Generation Networks with Integrated Photonic Digital‐to‐Analog Converters

Digital‐to‐analog converters (DAC) are indispensable functional units in signal processing instrumentation and wide‐band telecommunication links for both civil and military applications. As photonic systems are capable of high data throughput and low latency, an increasingly found system limitation stems from the required domain crossing such as digital to analog and electronic to optical. A photonic DAC implementation, in contrast, enables a seamless signal conversion with respect to both energy efficiency and short signal delay, often requiring bulky discrete optical components and electric–optic transformation, hence introducing inefficiencies. Herein, a novel coherent parallel photonic DAC concept along with a 4‐bit experimental prototype capable of performing this DAC without optic–electric–optic domain crossing is introduced. This new paradigm guarantees a linear intensity weighting among bits when operating at high sampling rates (50 GHz), featuring an exceptional sampling efficiency (> 100 GS ) and small footprint (≈1 mm 2 ) in an 8‐bit implementation. Importantly, this photonic DAC enables seamless interfaces of next‐generation data processing hardware with high relevance in data centers, task‐specific compute accelerators such as neuromorphic engines, and network edge processing applications.

Meng, Jiawei↗

DOE OSTI2020

PyXAS – an open-source package for 2D X-ray near-edge spectroscopy analysis

In the synchrotron X-ray community, X-ray absorption near-edge spectroscopy (XANES) is a widely used technique to probe the local coordination environment and the oxidation states of specific elements within a sample. Although this technique is usually applied to bulk samples, the advent of new synchrotron sources has enabled spatially resolved versions of this technique (2D XANES). This development has been extremely powerful for the study of heterogeneous systems, which is the case for nearly all real applications. However, associated with the development of 2D XANES comes the challenge of analyzing very large volumes of data. As an example, a single 2D XANES measurement at a synchrotron can easily produce ~106 spatially resolved XANES spectra. Conventional manual analysis of an individual XANES spectrum is no longer feasible. Here, a software package is described that has been developed for high-throughput 2D XANES analysis. A detailed description of the software as well as example applications are provided.

25 ENERGY STORAGE↗

DOE OSTI2020

Carbon Isotope Composition and the NDVI as Phenotyping Approaches for Drought Adaptation in Durum Wheat: Beyond Trait Selection

High-throughput phenotyping platforms provide valuable opportunities to investigate biomass and drought-adaptive traits. We explored the capacity of traits associated with drought adaptation such as aerial measurements of the Normalized Difference Vegetation Index (NDVI) and carbon isotope composition (δ13C) determined at the leaf level to predict genetic variation in biomass. A panel of 248 elite durum wheat accessions was grown at the Maricopa Phenotyping platform (US) under well-watered conditions until anthesis, and then irrigation was stopped and plot biomass was harvested about three weeks later. Globally, the δ13C values increased from the first to the second sampling date, in keeping with the imposition of progressive water stress. Additionally, δ13C was negatively correlated with final biomass, and the correlation increased at the second sampling, suggesting that accessions with lower water-use efficiency maintained better water status and, thus, performed better. Flowering time affected NDVI predictions of biomass, revealing the importance of developmental stage when measuring the NDVI and the effect that phenology has on its accuracy when monitoring genotypic adaptation to specific environments. The results indicate that in addition to choosing the optimal phenotypic traits, the time at which they are assessed, and avoiding a wide genotypic range in phenology is crucial.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗

DOE OSTI2025

Single-Objective Airy Light-Sheet Imaging

Despite its massive potential, standard light-sheet imaging (LSI) faces key challenges, such as the incompatibility with common sample mounting techniques and low-resolution imaging. Single-objective LSI attempts to address these issues but often suffers from limited fields-of-view and throughput rates, or requires multiple optics that increase costs, alignment complexity, and losses. To overcome these challenges of standard single-objective LSI, we introduce single-objective Airy light-sheet imaging (SoALSI). SoALSI leverages the extraordinary self-acceleration properties of the Airy beam, achieving 5× higher imaging rates and enhanced imaging efficiency than standard single-objective LSI. Here, we demonstrate SoALSI’s versatility through rigorous contrast and resolution characterizations and by high-resolution imaging of diverse biological specimens, including malaria parasite-infected red blood cells and plant root tissue. SoALSI seamlessly integrates with any standard inverted microscope frame, enabling broader accessibility for the bioimaging community to explore biological processes in a wide range of specimens with enhanced resolution and imaging contrast.

airy beam↗

DOE OSTI2020

Flat-histogram extrapolation as a useful tool in the age of big data

In this work, we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurate estimates of physical properties across a broad range of conditions have been achieved using this approach, greatly reducing the number of simulations needed to explore a given system's behaviour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Real Time, In-line Monitoring of Hanford Tank Wastes - Year 1 Report

The team comprised of students, postdocs, early, mid and senior career scientists from Los Alamos National Laboratory, Savanah River National Laboratory, Georgia Tech and Florida International University, with the guidance of H2C, is developing a suite of in-line instruments for the Hanford high level waste (HLW) and low active waste (LAW) processes to provide near-real-time analysis of waste form physical properties and composition. The work builds on results from the recent DOE-ORP, EM Technology Development and other projects that demonstrated promise for the use of real-time in-line monitoring (RTIM) to measure chemical compositions of slurries of up to 20 weight % solids. The goal is for this instrument suite is to substantially reduce the need for sampling for process control. Sample waste, exposure associated with sample analysis, and the demand for an external laboratory facility would be greatly reduced. The throughput of waste treatment systems would be improved by elimination of the downtime caused by waiting for sample results. This translates into reduced process storage as process knowledge will be continuously updated in near-real-time. These breakthrough technologies would significantly reduce the life cycle cost and accelerate the schedule for the Hanford tank waste mission.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

The Profiled Feldman-Cousins Method for Confidence Interval Construction for the Nova 3-Flavor Oscillation Analysis

The small interaction cross-section of neutrinos makes experimental neutrino physics particularly responsive to technological advancements. A significant development leveraged by the NOvA experiment is large-scale parallel processing, enabling novel computational approaches to longstanding experimental challenges. Central to managing the resulting high-throughput data is NOvA’s implementation of the Freight Train model, designed for efficient data production and handling.This dissertation details the methodology and execution of the NOvA 2024 3-Flavor Oscillation Analysis, supported by a comprehensive dataset spanning ten years. It emphasizes frequentist results refined through the Feldman-Cousins (FC) technique, specifically addressing confidence interval corrections in parameter estimation. The computational intensity associated with Feldman-Cousins arises from extensive Monte Carlo simulations, which were substantially mitigated through parallel computing on the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), employing the MPI framework.To further enhance computational efficiency, an Importance Sampling method is introduced and evaluated, demonstrating significant potential to reduce complexity, particularly in exploring extreme parameter space regions. This thesis presents both the successful application of advanced computational resources and the development of sophisticated statistical techniques, aiming to enhance the precision and scope of neutrino oscillation analyses.

Dye ajdye11190@gmail.com, Andrew Joseph [Mississip↗

DOE OSTI2020

Combinatorial investigation of structural and optical properties of cation-disordered ZnGeN 2

Cation-disordered ZnGeN 2 shows promise for application as a blue-green emitter in light-emitting devices, but more foundational work is necessary to understand structure–property relationships. In this work, we present a combinatorial exploration of the experimental phase space of wurtzite (cation-disordered) ZnGeN 2 using high-throughput co-sputtering. Structure, morphology and optical properties are explored as a function of cation composition and synthesis temperature. ZnGeN 2 is found to crystallize in the wurtzite structure ranging from Zn-rich to Ge-rich compositions. X-ray diffraction refinements reveal a continuous shift in cell volume with off-stoichiometry, indicating alloy-like structural behavior. Furthermore, the optical absorption of all films examined is lower in energy than the value predicted for cation-ordered ZnGeN 2 , suggesting that cation disorder is decreasing the bandgap. Additionally, the absorption threshold shifts continuously to higher energy for Ge-rich samples, consistent with bandgap shifts due to alloy-like structural behavior. Defect formation energy diagrams are calculated to help guide understanding of off-stoichiometry from a defect complex perspective. This work paves the way toward use of ZnGeN 2 as a bandgap-tunable optoelectronic semiconductor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Single-Cell Discovery and Multiomic Characterization of Therapeutic Targets in Multiple Myeloma

Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

A six degrees of freedom femtosecond laser system for fabrication of small-scale mechanical property specimens

Here, we present the details of a novel ultra-short pulsed laser machining workstation that has been employed for high-throughput laser machining of small-scale mechanical property specimens. This system employs a six degrees of freedom hexapod positioning stage capable of macroscopic movements at high positional accuracy. We developed a methodology that uses quantitative image analysis to measure key parameters required to minimize the hexapod positioning and rotational error. Application of this system to laser machining of small-scale 316L stainless steel tensile specimens and ultra-high molecular weight polyethylene compressive specimens using eucentric tilt and rotation about the specimen axis will be shown, where serial laser milling at a specimen tilt angle of 10° was used to effectively eliminate any taper in the sample cross section that is typically found in laser machining.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Rapid Design and Manufacturing of High-Performance Materials for Turbine Blades

This research demonstrated the concept of carbide precipitation-strengthened refractory high entropy alloys (RHEA). The advantage of a precipitation strengthened alloy is all phases are in thermodynamic equilibrium promoting microstructural stability, and consequently retention of properties at elevated temperatures. Additionally, as with any precipitation strengthened – or age hardened - alloy, components can be heat-treated after manufacturing to manipulate the microstructure and optimize properties for performance. This is an advantage of the precipitation strengthened alloys over composites and ceramics materials, where the microstructure and properties are essentially fixed upon the initial materials manufacturing stage. High throughput (HT), multi-scale computer modeling was used to identifying novel RHEA compositions with desired characteristics needed for precipitation strengthening. Designs were validated by producing small ingots via are melting. The results showed that carbides precipitated and consequently, the strength of the alloys (measured in compression) increased after heat-treatment, which is the desired effect. The project also demonstrated the feasibility of producing articles from RHEA by additive manufacturing (AM). Electron beam melting (EBM) and laser direct energy deposition (L-DED) additive manufacturing methods, were explored with various processing parameters were interrogated for both methods. Sound (crack-free and dense) precipitation strengthened RHEA samples were produced via EDM AM, demonstrating the feasibility of the concept.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Comparison of Microbial Profiling and Tracer Testing for the Characterization of Injector-Producer Interwell Connectivities

Insufficient understanding of the microbial communities and associated microbial processes in geological reservoirs hinders the utilization of this rich data source for improved resource management. In this study, along with four interwell tracer tests at a 1478-m deep fractured crystalline-rock aquifer, we analyzed the microbial communities in the injected and produced water by high-throughput sequencing. The microbial community similarities across boreholes during an interwell flow scenario frequently encountered in reservoir development was explored. Despite the significant tracer recoveries (~30%) in all tracer tests and the cumulatively >100,000 L of exogenous water (carrying exogenous microbes) injected into the 10-m-scale reservoir, the overall structure of produced-fluid microbiome did not increasingly resemble that of the injectate. However, producers with better connectivity with the injector (based on tracer test results) did have more amplicon sequence variants (ASVs) that overlapped with those in the injectate. We identified possible drivers behind our observations and verified the practicality of repeated microbial sampling in the context of reservoir characterization and long-term monitoring. We highlight that injector-producer microbial profiling could provide insights on the relative connectivities across different producers with a given injector, and that the associated logistical needs may be comparable or even less than that of classic tracer tests.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗

DOE OSTI2020

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of timeseries transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

59 BASIC BIOLOGICAL SCIENCES↗