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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Regularized Differentiation for Bioburden Density Estimation in Planetary Protection

In this paper, we propose and investigate the performance of two novel shrinkage estimators for bioburden density estimation in planetary protection. The estimators are based on the regularized differentiation of a cumulative count of colony forming units collected throughout the data collecting session or the life cycle of the entire mission. The regularized differentiation recasts the problem of bioburden density estimation as a linear least squares problem. The least squares problem is then solved through regularization techniques, such as truncated singular value decomposition and penalized least squares. The regularization is necessary to avoid noise amplification during the differentiation of noisy data. The two regularization estimators are compared with four other commonly used estimators to simultaneously evaluate the means of multivariable independent Poisson distributions: the maximum likelihood, noninformative Bayes estimator with Jeffreys prior, Empirical Bayes using conjugate gamma-Poisson model with gamma parameters selected by method of moments, and the Clevenson-Zidek estimator. It is shown through computer-simulated data that the regularized differentiation based on ridge regression has the smallest mean-squared error among all estimators. The analysis of shrinkage mechanism implemented by regularized differentiation is performed, and it is shown that the regularized differentiation amounts to performing a weighted averaging of all the samples. The weights are determined by the regularization parameter automatically selected by the L-curve technique. Since the method of least squares makes no distributional assumptions about the data, it presents an attractive technique for bioburden density estimation when there are concerns about the misspecification of the distributional model. The paper concludes with the analysis of the bioburden data collected during InSight mission and directions for future work.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
DOE OSTI2023

All-polymer nanocomposites having superior strength, toughness and ultralow energy dissipation

Toughening polymers has attracted significant interest. Traditionally, polymer toughness is enhanced by constructing polymer networks or introducing sacrificial bonds into the chains between crosslink points. These strategies, though, introduce pronounced energy dissipation and associated heat, both of which are undesirable under long-term cyclic loading, for example at the interface of implants in the human body. By incorporating single-chain nanoparticles (SCNPs) into linear polymer chains to generate all-polymer nanocomposites (APNCs), we have been able to achieve high strength, high toughness with low energy dissipation. Using a combination of simulation and experimental results, we are advancing a “SCNPs effect” where tightly cross-linked SCNPs produce a modulus contrast to achieve strengthening and toughening. Benefitting from the soft interface, the penetrable and deformable SCNPs cause the surrounding polymer chains to move in concert, significantly reducing the interfacial friction to achieve low energy dissipation. The intramolecular cross-linking of the SCNPs and adhesion between the SCNPs and polymer matrix are critical for realizing such high-performance systems. Based on a Gaussian regression model and back propagation (BP) neural network, the mechanical strength can be predicted and is supported by simulations. Finally, the APNC concept described can be applied to elastomers and gels, broadening its utilization in high-cycle and low-dissipation applications, like soft robots, flexible sensors and cartilage replacements, and artificial heart valves.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantile-Specific Heritability of Inflammatory and Oxidative Stress Biomarkers Linked to Cardiovascular Disease

Purpose: Heritability (h 2 , the proportion of the phenotypic variance attributable to additive genetic effects) is traditionally assumed to be constant throughout the distribution of the phenotype. However, the heritabilities of circulating C-reactive protein, interleukin-6, plasminogen activator inhibitor type-1 (PAI-1), and monocyte chemoattractant protein-1 (MCP-1) concentrations depend upon whether the phenotype is high or low relative to their distributions (quantile-dependent expressivity), which may account for apparent gene–environment interactions. Whether the heritabilities of other inflammatory biomarkers linked to cardiovascular disease are quantile-dependent remain to be determined. Patients and Methods: Quantile-specific offspring-parent (β OP ) and full-sib regression slopes (β FS ) were estimated by applying quantile regression to the age- and sex-adjusted phenotypes of families surveyed as part of the Framingham Heart Study. Quantile-specific heritabilities were calculated as: h 2 =2β OP /(1+r spouse ) and h 2 ={(1+8r spouse β FS ) 0.5 – 1}/(2r spouse ). Results: Heritability (h 2 ± SE) of lipoprotein-associated phospholipase A2 (Lp-PLA 2 ) mass concentrations increased from 0.11 ± 0.03 at the 10 th percentile, 0.08 ± 0.03 at the 25 th , 0.12 ± 0.03 at the 50 th , 0.20 ± 0.04 at the 75 th , and 0.26 ± 0.06 at the 90 th percentile, or 0.0023 ± 0.0006 per each one-percent increase in the phenotype distribution (P linear trend = 0.0004). Similarly, h 2 increased 0.0029 ± 0.0011 (P linear trend = 0.01) for sP-selectin, 0.0032 ± 0.0009 (P linear trend = 0.0001) for soluble intercellular adhesion molecule 1 (sICAM-1), and 0.0026 ± 0.0006 for tumor necrosis factor receptor 2 (TNFR2) (P linear trend = 5.0 × 10 -6 ) per each one-percent increase in their distributions when estimated from β OP . Osteoprotegerin and soluble ST2 heritability also increased significantly with increasing percentiles of their distributions when estimated from β FS . Lp-PLA 2 activity, CD40 ligand, TNFα, interleukin-18, and myeloperoxidase heritability showed no significant quantile-dependence. Conclusion: The heritabilities of circulating Lp-PLA 2 -mass, sP-selectin, sICAM-1, TNFR2, osteoprotegerin and soluble ST2 concentrations are quantile-dependent, which may contribute to purported genetic modulations of: 1) sP-selectin’s relationships to venous thrombosis, pulmonary hypertension, type 2 diabetes and atorvastatin treatment; 2) sICAM-I’s relationships to brain abscess and atorvastatin treatment; and 3) Lp-PLA 2 ’s relationships to myocardial infarction and preeclampsia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Predicting fatigue from heart rate signatures using functional logistic regression

Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real-life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data-driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7-mile Grand Canyon rim-to-rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real-life application.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses

Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learning methods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with unform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10Gbps connections with 0-366ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss rates but become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.

Generalization Bounds↗
DOE OSTI2020

Efficient bifurcation and tabulation of multi-dimensional combustion manifolds using deep mixture of experts: An a priori study

This work describes and validates an approach for autonomously bifurcating turbulent combustion man-ifolds to divide regression tasks amongst specialized artificial neural networks (ANNs). This approach relies on the mixture of experts (MoE) framework, where each neural network is trained to be specialized in a given portion of the input space. The assignment of different input regions to the experts is determined by a gating network, which is a neural network classifier. In some previous studies, it has been demonstrated that bifurcation of a complex combustion manifold and fitting different ANNs for each part leads to better fits or faster inference speeds. However, the manner of bifurcation in these studies was based on heuristic approaches or clustering techniques. In contrast, the proposed technique enables automatic bifurcation using non-linear planes in high-dimensional turbulent combustion manifolds that are often associated with complex behavior due to different dominating physics in various zones. Furthermore, the proposed concept is validated using 4-dimensional (4D) and 5D flamelet tables, showing that the errors obtained with a given network size, or conversely the network size required to achieve a given accuracy, is considerably reduced. The effect of the number of experts on inference speed is also investigated, showing that by increasing the number of experts from 1 to 8, the inference time can be approximately reduced by a factor of two. Moreover, it is shown that the MoE approach divides the input manifold in a physically intuitive manner, suggesting that the MoE framework can elucidate high-dimensional datasets in a physically meaningful way.

42 ENGINEERING↗
DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS

The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited. This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender. The volatile organic compound (VOC) signatures from subjects’ hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model. The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects. The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Application of machine learning for optical emission spectroscopy data in NAGDIS-II

In this study, we applied machine learning to optical emission spectroscopy (OES) data and device parameters from the linear plasma device NAGDIS-II to explore the potential application of machine learning for predicting electron density, $n$ e , and temperature, $T$ e . The covered ranges of $n$ e and $T$ e , which were measured by an electrostatic probe, are 3.6 × 10 17 –2.4 × 10 19 m -3 and 0.3–7.1 eV, respectively. A three hidden layer neural network (NN) is introduced to model the relationship between $n$ e /$T$ e and the combination of line intensities, radial position, and device parameters. It is shown that the errors in $n$ e and $T$ e become 18.0 and 18.8%, respectively, which were almost the same level for the electrostatic probe, using all available data. Lasso regression and greedy algorithm are used to select the necessary line emissions. In conclusion, it is shown that four- or five-line intensities are sufficient to obtain almost the same quality as the one with all the other lines.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Combined full shape analysis of BOSS galaxies and eBOSS quasars using an iterative emulator

ABSTRACT Standard full-shape clustering analyses in Fourier space rely on a fixed power spectrum template, defined at the fiducial cosmology used to convert redshifts into distances, and compress the cosmological information into the Alcock–Paczynski parameters and the linear growth rate of structure. In this paper, we propose an analysis method that operates directly in the cosmology parameter space and varies the power spectrum template accordingly at each tested point. Predictions for the power spectrum multipoles from the TNS model are computed at different cosmologies in the framework of $\Lambda \rm {CDM}$. Applied to the final eBOSS QSO and LRG samples together with the low-z DR12 BOSS galaxy sample, our analysis results in a set of constraints on the cosmological parameters Ωcdm, H0, σ8, Ωb, and ns. To reduce the number of computed models, we construct an iterative process to sample the likelihood surface, where each iteration consists of a Gaussian process regression. This method is validated with mocks from N-body simulations. From the combined analysis of the (e)BOSS data, we obtain the following constraints: σ8 = 0.877 ± 0.049 and $\Omega _{\rm m}=0.304^{+0.016}_{-0.010}$ without any external prior. The eBOSS quasar sample alone shows a 3.1σ discrepancy compared to the Planck prediction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Parameter uncertainties for imperfect surrogate models in the low-noise regime

Abstract Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly underestimated and vanish in the large data limit. As misspecification is the main source of uncertainty for surrogate models of low-noise calculations, such as those arising in atomistic simulation, predictive uncertainties are systematically underestimated. We analyze the true generalization error of misspecified, near-deterministic surrogate models, a regime of broad relevance in science and engineering. We show that posterior parameter distributions must cover every training point to avoid a divergence in the generalization error and design a compatible ansatz which incurs minimal overhead for linear models. The approach is demonstrated on model problems before application to thousand-dimensional datasets in atomistic machine learning. Our efficient misspecification-aware scheme gives accurate prediction and bounding of test errors in terms of parameter uncertainties, allowing this important source of uncertainty to be incorporated in multi-scale computational workflows.

Swinburne, Thomas D. (ORCID:0000000232554257)↗
DOE OSTI2026

Identifying Differential Equations in Fourier Domain (FourierIdent)

We investigate identifying differential equations in the frequency domain. Fourier analysis is an important tool in theoretical analysis and numerical solvers of differential equations, yet there is limited work in exploring this connection in the identification of differential equations. This paper aims to identify the underlying differential equation in the frequency domain, from a given single realization of the differential equation perturbed by noise. Such setting imposes difficulties which are different from other identification methods where computation is carried out in the physical domain. We propose several ways to mitigate the challenges arising from noise in data and large differences in the magnitudes of frequency responses. The main takeaways are that identifying differential equations solely in the frequency domain is challenging, the method we propose is based on a form of domain partitions in the frequency domain, and this method shows benefits for complex data even with high level of noise. We introduce a Fourier feature denoising, and define the meaningful data region and the core regions of features to reduce the effect of noise in the frequency domain and to enhance the accuracy in coefficient identification. The proposed method is tested on various differential equations with linear, nonlinear, and high-order derivative feature terms, and shows advantages on complex data with many frequency modes, even under high level of noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Excessive Aurantiochytrium acetophilum docosahexaenoic acid supplementation decreases growth performance and breast muscle mass of broiler chickens

We report Docosahexaenoic acid (DHA) is an n-3 polyunsaturated fatty acid with health-promoting potential. This study was to investigate effects of supplemental DHA from Aurantiochytrium acetophilum on growth performance, health status, meat quality, and protein synthesis signaling of broiler chickens. Day-old male chicks were housed in an environmental control room (6 cages/treatment, 8 chicks/cage), and fed a corn-soybean meal basal diet supplemented with the DHA-rich A. acetophilum biomass (Heliae, Gilbert, AZ) at 0, 1, 2, and 4% (0, 1.7, 3.4 and 6.8 g DHA/kg diet) for 6 weeks. Growth performance was measured weekly. Blood samples were collected at weeks 3 and 6 (2 chicks/cage). Four tissues were sampled (2 chicks/cage) for biochemical and meat quality analyses. Data were analyzed by one-way ANOVA and regression. Compared with the control, the 4% A. acetophilum diet decreased (p < 0.05) body weight gain (19%) and gain to feed ratio (19%) during weeks 4–6. The A. acetophilum supplementation dose-dependently decreased (p < 0.05, R 2 = 0.21–0.54) plasma alanine amino transferase activity and glucose concentrations, but had little effect on plasma activity of alkaline phosphatase or concentrations of inorganic phosphorus and uric acid at weeks 3 and 6. Compared with the control, the 4% A. acetophilum diet decreased (p < 0.05) breast muscle weight by 21%, and down-regulated (p < 0.05) mRNA levels of mammalian target of rapamycin and ribosomal s6 protein (S6), and protein levels of phosphorylated S6 to S6 and phosphorylated S6 kinase beta 1 to S6 kinase beta 1. The A. acetophilum supplementation linearly increased (p < 0.01) lipid peroxidation (R 2 = 0.62–0.90) and hardness and chewiness (R 2 = 0.34–0.44) of breast and thigh muscles. In conclusion, supplemental 4% (6.8 g DHA/kg), but not 1 or 2%, of A. acetophilum impaired growth performance, breast muscle mass accumulation, and(or) protein synthesis signaling of broilers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Correction for Neutral Pressure-Driven Signal in Radiated Power Measurements on Proto-MPEX

A pinhole camera using four-channel resistive bolometers was designed and implemented on the Prototype Material Plasma Exposure eXperiment (Proto-MPEX) to measure the plasma radiated power. Quantification of the radiated power is required for comprehensive power balance assessments but faces unique challenges on linear plasma devices due to weak absolute signal, compared to typical use in toroidal fusion plasmas, and high neutral pressure changes. Around 1 mTorr, the pressure-driven signal contribution was shown to overwhelm the radiated power signal in Proto-MPEX, found to be ~3 μW . Since the pressure-driven signals could not be predicted from first principle arguments, the signals were corrected using empirical models that take two experimental pressure measurements and the raw bolometer signal to reveal the radiated power signals. Four “gas-only” Proto-MPEX shots were taken, without the radio frequency (rf) plasma discharge, to train, optimize, and validate empirical models. The data included two pressure measurements at different locations along with the bolometer signal. Multiple models were tested; partial least-squares regression with a cross correlation method to account for the time offset in the pressure measurements provided the best combination of accuracy and stability. The model was validated to have a modeling error of 0.0036 V, which was lower than 10% of the expected radiated power signal. The model was tested on a different “gas-only” shot with a complete time-series data. For plasma operation shots, the corrected bolometer measurements were compared with an absolute extreme ultraviolet photodiode measurement and similarities and differences are discussed.

Lee, Seungsup↗
DOE OSTI2025

Machine learning-assisted profiling of a kinked ladder polymer structure using scattering

Ladder polymers consisting of fused rings in the backbone have very limited conformational freedom, which results in very different properties from traditional linear polymers. However, accurately determining their size and chain conformations from solution scattering remains a challenge. Their chain conformations of kinked ladder polymers are largely governed by the structures and relative orientations or configurations of the repeat units, unlike conventional polymer chains whose bending angles between repeat units follow a unimodal Gaussian distribution. Meanwhile, traditional scattering models for polymer chains do not account for these unique structural features. This work introduces a novel approach that integrates machine learning with Monte Carlo simulations to construct a model that can describe the geometry of a type of kinked CANAL ladder polymers. We first develop a Monte Carlo simulation model for sampling the configuration space of CANAL ladder polymers, where each repeat unit is modeled as a biaxial segment. Then, we establish a machine learning-assisted scattering analysis framework based on Gaussian Process Regression. Finally, we conduct small-angle neutron scattering experiments on a CANAL ladder polymer solution to apply our approach. Our method uncovers structural features of such ladder polymers that conventional methods fail to capture.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2023

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

47 OTHER INSTRUMENTATION↗