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DOE OSTI2022

Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods

Summary An essential step in the analysis of single‐cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single‐cell predictive marker (SPmarker) to identify novel cell‐type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single‐cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non‐ Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell‐type marker genes from scRNA‐seq data and pave the way for cross‐species mapping of scRNA‐seq data in plants.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Genetic programming for understanding cognitive biases that generate polarization in social networks

Recent studies have applied agent-based models to infer human-interpretable explanations of individual-scale behaviors that generate macro-scale patterns in complex social systems. Genetic programming has proven to be an ideal explainable AI tool for this purpose, where primitives may be expressed in an interpretable fashion and assembled into agent rules. Evolutionary model discovery (EMD) is a tool that combines genetic programming and random forest feature importance analysis, to infer individual-scale, human-interpretable explanations from agent-based models. We deploy EMD to investigate the cognitive biases behind the emergence of ideological polarization within a population. An agent-based model is developed to simulate a social network, where agents are able to create or sever links with one another, and update an internal ideological stance based on their neighbors' stances. Agent rules govern these actions and constitute of cognitive biases. A set of 7 cognitive biases are included as genetic program primitives in the search for rules that generate hyper-polarization among the population of agents. We find that heterogeneity in cognitive biases is more likely to generate polarized social networks. Highly polarized social networks are likely to emerge when individuals with confirmation bias are exposed to those with either attentional bias, egocentric bias, or cognitive dissonance.

Gunaratne, Chathika↗

DOE OSTI2021

Mathematical modeling and parameter estimation of MFI membranes for para/ortho‐xylene separation

Abstract A mathematical model of a membrane with a thin, oriented, and selective MFI layer, which also includes contributions from defects, pore blockages, support layer, and external mass transfer, was formulated based on the Maxwell‐Stefan equations. It was validated using reported (Kim et al., Angew Chemie Int Ed, 2018, 57:480–485; Jeon et al., Nature, 2017, 543:690–694) para/ortho‐xylene separation data from five MFI membranes. The diffusivities of the xylenes were considered to be the same for all membranes, while the thickness and the density of defects and pore blockage were treated as unique properties of each membrane, and their contributions were estimated by fitting the model to the corresponding separation data. The effects of these properties and the role of permeate pressure on the separation performance were subsequently analyzed. The proposed modeling, parameter estimation, and analysis framework allow one to quantitatively interpret the variation of separation performance, to understand separation bottlenecks, and to provide guidance for designing membranes with desired performance.

Liu, Jingjun↗

DOE OSTI2022

A comparison of building system parameters between affordable and market-rate housing in New York City

Low-income households in the United States experience higher than average energy burdens (defined as the proportion of household income spent on energy utilities), and many of these households struggle to simultaneously pay for rent, energy, and basic household necessities. The analysis presented here in this study examines whether the underlying characteristics of buildings and their energy systems could contribute to this disparity for affordable housing residents in New York City. It combines an energy audit dataset of 7,328 multifamily buildings with a database of properties receiving local, state, or federal housing subsidies. The results of this analysis indicate that the building-level installed equipment in large (greater than 50,000 square feet) affordable housing buildings in New York City is more efficient than that in market-rate buildings, but this trend largely disappears when considering overall building characteristics, such as location, size, or age. Significant differences in the types of systems installed in affordable and market-rate housing are also observed, as well as the types of energy efficiency recommendations made by energy auditors. However, these latter data were not normalized by building system characteristics, as that analysis is much more difficult to interpret for categorical data such as heating system type. These findings indicate that retrofit policies and building performance standards focused on affordable housing will likely need to account for underlying differences in building characteristics between affordable and market-rate housing to achieve intended impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Structural characterization of high-protein system through ultra-small and small-angle X-ray scattering

High-protein systems exhibit a hierarchical structure consisting of interconnected multiscale length assemblies. Recent investigations are directed toward identifying these structural units under various processing and environmental conditions to establish structure–function correlations. Ultra-small and small-angle X-ray scattering (USAXS/SAXS) has become a crucial tool for characterizing the structures of proteins and their clusters/aggregates, ranging from nanometers to micrometers, with minimal disruption to their original state. Here, this review first describes the facilities and principles of X-ray scattering, followed by discussions on the analysis of scattering data, including the interpretation of fitting models. It then delves into the main applications of USAXS/SAXS in plant and dairy protein-rich systems. Future strategies to enhance the utilization of scattering techniques for elucidating the structure of high-protein systems are also included.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Data-Driven Discovery of Linear Molecular Probes with Optimal Selective Affinity for PFAS in Water

Approaches to tackle the wide and growing variety of highly persistent per- and polyfluoroalkyl substances (PFAS) are of pressing global need because of their detrimental human health effects, such as cancer, birth defects, and hormone imbalance. Sensitive, selective, and easy-to-use real-time sensors to monitor and detect PFAS and sorbents to extract them are critical to meeting government-mandated environmental concentrations. In this work, we combine all-atom molecular dynamics simulations, enhanced sampling, deep representational learning, and Bayesian optimization to perform high-throughput virtual screening for highly sensitive and selective molecular probes. Our molecular design space consists of 3850 linear hydrocarbon chains with varying degrees of halogenation with and without amine- and phosphine-based headgroups. By employing a data-driven search process, we efficiently explore the molecular design space to optimize the sensitivity to perfluorooctanesulfonic acid (PFOS) as a prototypical PFAS analyte and selectivity relative to a sodium dodecyl sulfate (SDS) interferent. We calculate 504 Gibbs free energies of probe-analyte and probe-interferent interactions and identify probes with PFOS association free energies of up to (-ΔG PFOS ) = 9.8 ± 0.2 kJ/mol and selectivities relative to SDS of (-ΔΔG PFOS–SDS ) = 3.1 ± 1.5 kJ/mol. A C 11 Br 23 P(CH 3 ) 2 probe containing 11 backbone brominated carbons and a tertiary phosphine headgroup possesses the most sensitive binding constant to PFOS within the defined search space of K b PFOS = 177.4 ± 12.7, and a semibrominated probe C 5 H 11 C 7 Br 14 N(CH 3 ) 2 containing 12 backbone carbons and a tertiary amine headgroup possesses the highest selectivity relative to SDS of K b PFOS /K b SDS = 4.6 ± 1.7. A retrospective analysis of our data to extract interpretable design rules reveals that the sensitivity of linear hydrogenated probes increases by approximately 1 kJ/mol per C–C bond. The addition or removal of halogen atoms and amine or phosphine headgroups produces nonmonotonic changes in both sensitivity and selectivity with changes to the sensitivity of up to 2.5 kJ/mol. Finally, this work places empirical limitations on the performance of a wide range of linear probes for PFOS detection and offers a generic strategy for high-throughput computational screening to promote selective and sensitive binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Genetic algorithm-based geometry calibration for dynamic compression x-ray diffraction experiments

An important component of dynamic compression x-ray diffraction (XRD) experiment analysis is geometry calibration: proper data interpretation requires knowledge of the precise detector position and orientation and, if the experiment involves a single-crystal sample, knowledge of the lattice orientation. The determination of these parameters in the arbitrary three-dimensional (3D) scattering geometries often present in dynamic compression facilities is challenging, as the associated optimization problem can be highly nonlinear, nonsmooth, and discontinuous. We present a genetic algorithm-based approach for performing dynamic compression XRD calibrations that overcomes these obstacles. We provide details regarding the image processing, algorithm implementation, and open-source software deployment and demonstrate the capability of the approach to calibrate the detector and crystal parameters in 3D geometries. Notably, we demonstrate the solver’s capacity to find the crystal orientation without a priori rotation constraints.

Brown, Nathan P. [Sandia National Laboratories (SN↗

DOE OSTI2020

Exceptionally large anomalous Hall effect due to anticrossing of spin-split bands in the antiferromagnetic half-Heusler compound TbPtBi

We have investigated the magnetotransport properties and the topological electronic structure of the half-Heusler compound TbPtBi. Our experiments reveal an exceptionally large anomalous Hall effect (AHE) in the canted antiferromagnetic state of TbPtBi with the anomalous Hall angle reaching ~0.68–0.76, which is a few times larger than the previously reported record in GdPtBi. First-principles electronic structure and the associated anomalous Hall conductivity were computed in order to interpret the experimental results. Furthermore, our analysis shows that the AHE in TbPtBi does not originate from the Weyl points but that it is driven by the large net Berry curvature produced by the anticrossing of spin-split bands near the Fermi level in TbPtBi.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Interactively Assessing Disentanglement in GANs

Abstract Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo‐realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute‐based semantic scoring of images produced by GANs. Through use‐cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.

Jeong, Sangwon↗

DOE OSTI2026

Machine learning of factors for improving oyster hatchery production

Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.

Vishwakarma, Srishti [Oak Ridge National Laborator↗

DOE OSTI2026

Linear viscoelastic measurements of commerical therapy putties for design and intuition

This dataset complements the publication Mapping Linear Viscoelasticity for Design and Tactile Intuition (Corman & Ewoldt, 2019). It contains rigorous shear rheometry measurements for six commercial silicone putties, including creep recovery, SAOS, LAOS, and stress relaxation. Additional, unpublished data was generated as part of a graduate rheology course taught by Randy Ewoldt at University of Illinois Urbana-Champaign. Together, these datasets provide a comprehensive example of linear viscoelasticity of a commercial material. The collection is intended both as a pedagogical resource for developing physical intuition in linear viscoelasticity and as a reference dataset for data interpretation, model fitting, and comparative rheological analysis.

C. Marsh, Maxwell↗

DOE OSTI2021

Reference document for LANL stack sampling and ANSI N13.1 (Article) Gielow RL and McNamee MR 1993. Numerical Flue Gas Flow Modeling for Continuous Emissions Monitoring Applications. EPRI CEM Users Group Meeting. Baltimore. RP1961-13

American National Standard N13.1 “sets forth guidelines and performance criteria for sampling the emissions of airborne radioactive substances in the air discharge ducts and stacks of nuclear facilities. Emphasis is on extractive sampling from a location in a stack or duct where the contaminant is well mixed. At such a location, sampling may be conducted at a single point. This standard provides performance-based criteria for the use of air sampling probes, transport lines, sample collectors, sample monitoring instruments, and gas flow measuring methods. This standard also covers sampling program objectives, quality assurance issues, developing air sampling action levels, system optimization, and system performance verification. Workplace, containment, and environmental air monitoring are not addressed. Specific sample analysis methods and the reporting or interpreting of results are also not addressed.” (HPS 2011).

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2026

Investigating the Impacts of Aqueous-Phase Processing on Organic Aerosol Chemical Climatology Using ARM and ASR Observations

This project improved understanding of how atmospheric aerosol particles form and evolve, with a focus on the role of water-driven (aqueous-phase) chemical reactions in the atmosphere. These processes occur in clouds, fog, and humid air and can significantly change the composition and properties of airborne particles, known as aerosols, which influence air quality and climate. By combining field measurements, laboratory experiments, and advanced analytical techniques, the project identified key chemical signatures that allow scientists to distinguish particles formed through aqueous processes from those formed in the gas phase. Observations from multiple environments, including wildfire smoke, urban regions, and cloud-influenced areas, show that aqueous chemistry is an important pathway for particle formation and aging. The project also developed new measurement approaches using uncrewed aerial systems (UAS) to capture how aerosol composition varies with altitude, providing critical insights into how particles interact with clouds. In addition, new data analysis frameworks were created to better interpret long-term aerosol measurements and improve characterization of particle sources and transformations. These results have been integrated into a global database of aerosol measurements and used to support atmospheric modeling efforts. Overall, the project provides important tools and knowledge to improve predictions of how aerosols affect climate and air quality, particularly through their interactions with radiation and clouds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Misinterpreting modified gravity as dark energy: a quantitative study

Standard cosmological data analyses typically constrain simple phenomenological dark-energy parameters, for example the present-day value of the equation of state parameter, w 0 , and its variation with scale factor, w a . However, results from such an analysis cannot easily indicate the presence of modified gravity. Even if general relativity does not hold, experimental data could still be fit sufficiently well by a phenomenological w 0 w a CDM, unmodified-gravity model. Hence, it would be very useful to know if there are generic signatures of modified gravity in standard analyses. Here we present, for the first time to our knowledge, a quantitative mapping showing how modified gravity models look when (mis)interpreted within the standard unmodified-gravity analysis. Scanning through a broad space of modified-gravity (Horndeski) models, and assuming a near-future survey consisting of CMB, BAO, and SNIa observations, we report values of the best-fit set of cosmological parameters including (w 0 , w a ) that would be inferred if modified gravity were at work. We find that modified gravity models that can masquerade as standard gravity lead to very specific biases in standard-parameter spaces. Here, we also comment on implications for measurements of the amplitude of mass fluctuations described by the parameter S 8 .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films

Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called “better”, “faster”, and “less-biased” ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) β-variational autoencoders (β-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. Further, to improve the interpretability of the latent space, a variational Kullback–Leibler-divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.

36 MATERIALS SCIENCE↗

DOE OSTI2021

DEEPEN Data Catalog for Magmatic Geothermal Systems in the United States

This data catalog contains information related to the Training Site Analysis for the Geothermica project "DE-risking Exploration of geothermal Plays in magmatic ENvironments (DEEPEN)." The DEEPEN project aims to reduce exploration risk for geothermal fluids in magmatic systems by developing improved an improved framework for interpretation of exploration data using the Play Fairway Analysis (PFA) methodology. The Training Site Analysis performed for DEEPEN leverages existing datasets to develop a customized PFA approach to exploration for multiple geothermal resource types in magmatic systems (conventional hydrothermal resources, supercritical fluid and superheated steam resources, and superhot EGS resources). This data catalog contains links to publicly available data files related to 8 training sites in the United States. US training sites are: the Cascades/Aleutians PFA project; the Hawaii PFA project, the Oregon Cascades PFA project, the Snake River Plain, Idaho PFA project, the Washington State PFA project, Newberry Volcano, Coso Geothermal Field, and the Geysers Geothermal field. This database contains an overview of these training sites, data sources, and links to publicly available exploration datasets. For the five PFA projects, details on exploration data related to PFA components (heat, fluid, permeability, sometimes seal) are provided, including a summary of data weighting methodologies.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Subsurface Trend Analysis domains for the northern Gulf of Mexico

Geologic domains for the northern Gulf of Mexico derived using the Subsurface Trend Analysis (STA) method. The domains were postulated using geologic province, lithologic, and structural information and validated using statistical methods. Publication detailing the STA method: Rose, K., Bauer, J.R., and Mark-Moser, M. (2020) Subsurface trend analysis, a multi-variate geospatial approach for subsurface evaluation and uncertainty reduction, Interpretation, vol. 8, issue 1 https://library.seg.org/doi/abs/10.1190/int-2019-0019.1 Detailed discussion of domain formation and analysis: Mark-Moser, M., Miller, R., Bauer, J., Rose, K., and C. Disenhof. 2018, Analysis of Subsurface Reservoir Properties Using a Novel Geospatial Approach, Offshore Gulf of Mexico. NETL-TRS-2018 https://edx.netl.doe.gov/dataset/detailed-analysis-of-geospatial-trends-of-hydrocarbon-accumulations-offshore-gulf-of-mexico

geology↗

DOE OSTI2020

Interpreting Morphological Adaptations Associated with Viviparity in the Tsetse Fly Glossina morsitans (Westwood) by Three-Dimensional Analysis

Tsetse flies (genus Glossina), the sole vectors of African trypanosomiasis, are distinct from most other insects, due to dramatic morphological and physiological adaptations required to support their unique biology. These adaptations are driven by demands associated with obligate hematophagy and viviparous reproduction. Obligate viviparity entails intrauterine larval development and the provision of maternal nutrients for the developing larvae. The reduced reproductive capacity/rate associated with this biology results in increased inter- and intra-sexual competition. Here, we use phase contrast microcomputed tomography (pcMicroCT) to analyze morphological adaptations associated with viviparous biology. These include (1) modifications facilitating abdominal distention required during blood feeding and pregnancy, (2) abdominal and uterine musculature adaptations for gestation and parturition of developed larvae, (3) reduced ovarian structure and capacity, (4) structural features of the male-derived spermatophore optimizing semen/sperm delivery and inhibition of insemination by competing males and (5) structural features of the milk gland facilitating nutrient incorporation and transfer into the uterus. Three-dimensional analysis of these features provides unprecedented opportunities for examination and discovery of internal morphological features not possible with traditional microscopy techniques and provides new opportunities for comparative morphological analyses over time and between species.

59 BASIC BIOLOGICAL SCIENCES↗