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DOE OSTI2020

Local structure graph models with higher-order dependence

Local structure graph models (LSGMs) describe random graphs and networks as a Markov random field (MRF)—each graph edge has a specified conditional distribution dependent on explicit neighbourhoods of other graph edges. Centered parameterizations of LSGMs allow for direct control and interpretation of parameters for large- and small-scale structures (e.g., marginal means vs. dependence). Here, we extend this parameterization to account for triples of dependent edges and illustrate the importance of centered parameterizations for incorporating covariates and interpreting parameters. Using a MRF framework, common exponential random graph models are also shown to induce conditional distributions without centered parameterizations and thereby have undesirable features. This work attempts to advance graph models through conditional model specifications with modern parameterizations, covariates and higher-order dependencies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Implementation of AI/DEEP learning disruption predictor into a plasma control system

Abstract This paper reports on advances in the state‐of‐the‐art deep learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced in a 2019 NATURE publication [ https://doi.org/10.1038/s41586‐019‐1116‐4 ]. In particular, the predictor now features not only the “disruption score,” as an indicator of the probability of an imminent disruption, but also a “sensitivity score” in real time to indicate the underlying reasons for the imminent disruption. This adds valuable physics interpretability for the deep learning model and can provide helpful guidance for control actuators now implemented into a modern plasma control system (PCS). The advance is a significant step forward in moving from modern deep learning disruption prediction to real‐time control and brings novel AI‐enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII‐D vetted in the earlier NATURE publication. In addition to “when” a shot is predicted to disrupt, this paper addresses reasons “why” by carrying out sensitivity studies. FRNN is accordingly extended to use more channels of information, including measured DIII‐D signals such as (i) the “n1rms” signal that is correlated with the n = 1 modes with finite frequency, including neoclassical tearing mode and sawtooth dynamics; (ii) the bolometer data indicative of plasma impurity control; and (iii) “q‐min”—the minimum value of the safety factor relevant to the key physics of kink modes. The additional channels and interpretability features expand the ability of the deep learning FRNN software to provide information about disruption subcategories as well as more precise and direct guidance for the actuators in a PCS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Evidence of Free-Bound Transitions in Warm Dense Matter and Their Impact on Equation-of-State Measurements

Warm dense matter (WDM) is now routinely created and probed in laboratories around the world, providing unprecedented insights into conditions achieved in stellar atmospheres, planetary interiors, and inertial confinement fusion experiments. However, the interpretation of these experiments is often filtered through models with systematic errors that are difficult to quantify. Due to the simultaneous presence of quantum degeneracy and thermal excitation, transitions in which free electrons are de-excited into thermally unoccupied bound states transferring momentum and energy to a scattered x-ray photon become viable. Here we show that such free-bound transitions are a particular feature of WDM and vanish in the limits of cold and hot temperatures. The inclusion of these transitions into the analysis of recent X-ray Thomson Scattering experiments on WDM at the National Ignition Facility and the Linac Coherent Light Source is required to obtain a physically consistent temperature from the Chihara decomposition. This interpretation is corroborated by agreement with a recently developed model-free thermometry technique and presents an important step for precisely characterizing and understanding the complex WDM state of matter.

Boehme, M [HZDR Dresden]↗

DOE OSTI2021

Engineering evaluation of the EQSIM simulated ground-motion database: The San Francisco Bay Area region

Ground-motion simulations for infrequent earthquake scenarios are gaining increasing interest in the engineering community for their potential to inform performance-based structural design and assessment, particularly in regions where major earthquakes are expected, but recordings from consistent historical events are not available. However, the absence of empirical data makes the evaluation of such scenarios and the interpretation of the simulation results very challenging. In this context, this paper presents the evaluation of the first EQSIM (v.1.1.0) ground-motion simulations generated for the San Francisco Bay Area (SFBA) region. The current database, which is at the first stages of development, includes eight realizations of a Hayward Fault M w 7 event. The evaluation of the simulated motions is first conducted on the average ground-motion amplitudes through comparison against the NGA-W2 GMPEs and a selected population of real records. A methodology for selecting a population of real records consistent with the simulated event is proposed. The objective of the proposed evaluation is twofold: (1) build confidence in the realistic character of the simulated motions for their use in engineering applications and (2) offer a critical physics-based interpretation of the simulation results that can help improve key features of the simulation models. To further investigate the implications of using the simulated ground motions for site-specific structural assessments, the ASCE7-16 approach is employed for the analysis of two hazard-consistent near-field sites in the SFBA. Results are discussed. Furthermore, this study offers a critical review of the EQSIM (v.1.1.0) SFBA ground-motion simulations and suggestions for improving the earthquake rupture models.

58 GEOSCIENCES↗

DOE OSTI2022

Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation

Abstract A unified conceptual framework for river corridors requires synthesis of diverse site‐, method‐ and discipline‐specific findings. The river research community has developed a substantial body of observations and process‐specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site‐, scale‐, and mechanism‐specific observations begins the process of synthesis. Machine learning is particularly well‐suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively‐generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Combined quantum‐mechanical molecular mechanics calculations with NWChem and AMBER: Excited state properties of green fluorescent protein chromophore analogue in aqueous solution

Combined quantum mechanical molecular mechanics (QM/MM) calculations have become a popular methodology for efficient and accurate description of large molecular systems. In this work we introduce our development of a QM/MM framework based on two well‐known codes—NWChem and AMBER. As an initial application area we are focused on excited state properties of small molecules in an aqueous phase using an analogue of the green fluorescent protein (GFP) chromophore as a particular test case. Our approach incorporates high level coupled cluster theory for the analysis of excited states providing a reliable theoretical analysis of effects of an aqueous solvation environment on the photochemical properties of the GFP chromophore. Using a systematic approach, which involves comparison of gas phase and aqueous phase results for different protonation states and conformations, we resolve existing uncertainties regarding the theoretical interpretation of experimental data. We observe that the impact of aqueous environment on charged states generally results in blue shifts of the absorption spectra, but the magnitude of the effect is sensitive to both protonation state and conformation and can be rationalized based on charge movement into the area of higher/lower external electrostatic potentials. At neutral pH levels the experimentally observed absorption signal is most likely coming from the phenol protonated form. Our results also show that the high level electron correlated method is essential for a proper description of excited states of GFP. © 2017 Wiley Periodicals, Inc.

Pirojsirikul, Teerapong↗

DOE OSTI2025

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Differences in muscle contraction onset as determined by ultrasound and electromyography

ABSTRACT Introduction We characterize the agreement between the timing of muscle contraction onset detected by surface electromyography (sEMG), fine wire EMG (fwEMG), and motion‐mode (M‐mode) ultrasound for improved interpretations of clinical outcomes. Methods Eighteen healthy adults participated. Differences in contraction onset were compared between sEMG, fwEMG, and M‐mode ultrasound collected during concentric contractions of the vastus lateralis and biceps brachii. Results The mean difference of 13.1 ms (‐33.3–59.9) between sEMG and fwEMG was non‐significant (intraclass correlation [ICC] = 0.60). Ultrasound was significantly different from surface and fine wire EMG (ICC = 0.65 and ICC = 0.40, respectively), occurring 98.6 ms (72.3–124.9) and 111.7 (60.3–163.0) before sEMG and fwEMG, respectively. Nonparametric interquartile ranges were also wide. Conclusions Due to high variability, comparisons between EMG methods should be interpreted with caution. Ultrasound detected onset before either EMG method, which may indicate motion from adjacent muscles during voluntary contractions. Muscle Nerve 59:494–500, 2019

Tweedell, Andrew J.↗

DOE OSTI2018

Validation of Spawn Checks and Saltwater Age Assignments Based on Scales from Known Repeat-Spawning Steelhead

Abstract It is critical to resolve the effects of regeneration, false annuli, and resorption on interpretation of scale patterns. We used scales collected from known repeat-spawning steelhead Oncorhynchus mykiss from the Snake River to (1) determine the accuracy of spawn check identification and (2) investigate errors in assignment of saltwater ages before and after spawn checks. Scale readers (n = 4) accurately identified over 96% of known repeat spawners (n = 107) and did not identify spawn checks in known first-time spawners (n = 197). Scale resorption associated with the spawn check caused an obscured or lost saltwater annulus in 66% of samples, most frequently in fish that spent 2 years versus 1 year in saltwater. Presence or absence of an annulus after the spawn check was accurately assigned in 86% of samples (n = 100). The potential for scale resorption to affect interpretation of the life history of iteroparous anadromous salmonids is significant. We supply two examples of how circulus counts can provide guidance to scale readers, but this analysis should be customized to the growth characteristics of each stock of interest.

Copeland, Timothy↗

DOE OSTI2024

Optical Diagnostics of Discharges in and in Contact With Liquids

Discharges in liquids have been the subject of intensive investigations for a broad range of applications and involve highly coupled and complex phenomena with timescales ranging from picoseconds to seconds and length scales from micrometers to centimeters. This review focuses on some of the challenges in optical diagnostics of discharges in and in contact with liquids; this includes the interpretation of optical emission spectra, the inherently stochastic nature of many discharge phenomena including the initial stages of plasma ignition, and the large gradients in species and plasma properties interfacing with a dynamic liquid interface. We report recent notable advances, controversies in interpretations of diagnostics, and remaining opportunities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Probing eddy size and its effective mixing length in stably stratified roughness sublayer flows

Stably stratified roughness sublayer flows are ubiquitous yet remain difficult to represent in models and to interpret using field experiments. Here, continuous high-frequency potential temperature profiles from the forest floor up to 6.5 times the canopy height observed with distributed temperature sensing (DTS) are used to link eddy topology to roughness sublayer stability correction functions and coupling between air layers within and above the canopy. The experiments are conducted at two forest stands classified as hydrodynamically sparse and dense. Near-continuous profiles of eddy sizes (length scales) and effective mixing lengths for heat are derived from the observed profiles using a novel conditional sampling approach. The approach utilizes potential temperature isoline fluctuations from a statically stable background state. The transport of potential temperature by an observed eddy is assumed to be conserved (adiabatic movement) and we assume that irreversible heat exchange between the eddy and the surrounding background occurs along the (vertical) periphery of the eddy. This assumption is analogous to Prandtl's mixing-length concept, where momentum is transported rapidly vertically and then equilibrated with the local mean velocity gradient. A distinct dependence of the derived length scales on background stratification, height above ground, and canopy characteristics emerges from the observed profiles. Implications of these findings for (1) the failure of Monin–Obukhov similarity in the roughness sublayer and (2) above-canopy flow coupling to the forest floor are examined. The findings have practical applications in terms of analysing similar DTS data sets with the proposed approach, modelling roughness sublayer flows, and interpreting nocturnal eddy covariance measurements above tall forested canopies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

A Mass Spectrometry-Based Predictive Strategy Reveals ADAP1 is Phosphorylated at Tyrosine 364

The goal of this work was to identify phosphorylation sites within the amino acid sequence of human ADAP1. Using traditional mass spectrometry-based techniques we were unable to produce interpretable spectra demonstrating modification by phosphorylation. This prompted us to employ a strategy in which phosphorylated peptides were first predicted using peptide mapping followed by targeted MS/MS acquisition. ADAP1 was immunoprecipitated from extracts of HEK293 cells stably-transfected with ADAP1 cDNA. Immunoprecipitated ADAP1 was digested with proteolytic enzymes and analyzed by LC-MS in MS1 mode by high-resolution quadrupole time-of-flight mass spectrometry (QTOF-MS). Peptide molecular features were extracted using an untargeted data mining algorithm. Extracted peptide neutral masses were matched against the ADAP1 amino acid sequence with phosphorylation included as a predicted modification. Peptides with predicted phosphorylation sites were analyzed by targeted LC-MS2. Acquired MS2 spectra were then analyzed using database search engines to confirm phosphorylation. Spectra of phosphorylated peptides were validated by manual interpretation. Further confirmation was performed by manipulating phospho-peptide abundance using calf intestinal phosphatase (CIP) and the phorbol ester, phorbol 12-myristate 13-acetate (PMA). Of five predicted phosphopeptides, one, comprised of the sequence AVDRPMLPQEYAVEAHFK, was confirmed to be phosphorylated on a Tyrosine at position 364. Pre-treatment of cells with PMA prior to immunoprecipitation increased the ratio of phosphorylated to unphosphorylated peptide as determined by area counts of extracted ion chromatograms (EIC). Addition of CIP to immunoprecipitation reactions eliminated the phosphorylated form. A novel phosphorylation site was identified at Tyrosine 364. Phosphorylation at this site is increased by treatment with PMA. PMA promotes membrane translocation and activation of protein kinase C (PKC), indicating that Tyrosine 364 is phosphorylated by a PKC-dependent mechanism.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

What can we learn from N 2 O isotope data? – Analytics, processes and modelling

The isotopic composition of nitrous oxide (N 2 O) provides useful information for evaluating N 2 O sources and budgets. Due to the co–occurrence of multiple N 2 O transformation pathways, it is, however, challenging to use isotopic information to quantify the contribution of distinct processes across variable spatiotemporal scales. Here, we present an overview of recent progress in N 2 O isotopic studies and provide suggestions for future research, mainly focusing on: analytical techniques; production and consumption processes; and interpretation and modelling approaches. Comparing isotope–ratio mass spectrometry (IRMS) with laser absorption spectroscopy (LAS), we conclude that IRMS is a precise technique for laboratory analysis of N 2 O isotopes, while LAS is more suitable for in situ/inline studies and offers advantages for site–specific analyses. When reviewing the link between the N 2 O isotopic composition and underlying mechanisms/processes, we find that, at the molecular scale, the specific enzymes and mechanisms involved determine isotopic fractionation effects. In contrast, at plot–to–global scales, mixing of N 2 O derived from different processes and their isotopic variability must be considered. We also find that dual isotope plots are effective for semi–quantitative attribution of co–occurring N 2 O production and reduction processes. More recently, process–based N 2 O isotopic models have been developed for natural abundance and 15 N–tracing studies, and have been shown to be effective, particularly for data with adequate temporal resolution. Here, despite the significant progress made over the last decade, there is still great need and potential for future work, including development of analytical techniques, reference materials and inter–laboratory comparisons, further exploration of N 2 O formation and destruction mechanisms, more observations across scales, and design and validation of interpretation and modelling approaches. Synthesizing all these efforts, we are confident that the N 2 O isotope community will continue to advance our understanding of N 2 O transformation processes in all spheres of the Earth, and in turn to gain improved constraints on regional and global budgets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Fourier Neural Networks as Function Approximators and Differential Equation Solvers

We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use.

Fourier decomposition↗

DOE OSTI2016

Information or resolution: Which is required from an SEM to study bulk inorganic materials?: Evaluate SEMs’ practical performance

Significant technological advances in scanning electron microscopy (SEM) have been achieved over the past years. Different SEMs can have significant differences in functionality and performance. This work presents the perspectives on selecting an SEM for research on bulk inorganic materials. Understanding materials demands quantitative composition and orientation information, and informative and interpretable images that reveal subtle differences in chemistry, orientation/structure, topography, and electronic structure. The capability to yield informative and interpretable images with high signal-to-noise ratios and spatial resolutions is an overall result of the SEM system as a whole, from the electron optical column to the detection system. The electron optical column determines probe performance. The roles of the detection system are to capture, filter or discriminate, and convert signal electrons to imaging information. The capability to control practical operating parameters including electron probe size and current, acceleration voltage or landing voltage, working distance, detector selection, and signal filtration is inherently determined by the SEM itself. As a platform for various accessories, e.g. an energydispersive spectrometer and an electron backscatter diffraction detector, the properties of the electron optical column, specimen chamber, and stage greatly affect the performance of accessories. Ease-of-use and ease-of-maintenance are of practical importance. It is practically important to select appropriate test specimens, design suitable imaging conditions, and analyze the specimen chamber geometry and dimensions to assess the overall functionality and performance of an SEM. Finally, for an SEM that is controlled/operated with a computer, the stable software and user-friendly interface significantly affect the usability of the SEM.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Surface Analysis Insight Note: Observations relating to photoemission peak shapes, oxidation state, and chemistry of titanium oxide films

It is common practice to describe the coordination of metal atoms in a binding configuration with their nearest neighbors in terms of oxidation state, a measure by which the number of electrons redistributed between atoms forming chemical bonds. In XPS terms, change to an oxidation state is commonly inferred by correlating photoemission signal with binding energy. The assumption, when classifying photoemission signals into distinct spectral shapes, is that a distribution of intensities shifted to lower binding energy is evidence of a reduction in oxidation state. In this Insight note, we raise the prospect that changes in photoemission peak shape may occur without obvious changes, determined by XPS in stoichiometry for a material. It is well known that TiO 2 measured by XPS yields reproducible Ti 2p photoemission peaks. However, on exposing TiO 2 to ion beams, Ti 2p photoemission evolves to complex distributions in intensity, which are particularly difficult to analyze by traditional fitting of bell-shaped curves to these data. For these reasons, in this Insight note, a thin film of TiO 2 deposited on a silicon substrate is chosen for analysis by XPS and linear algebraic techniques. Alterations in spectral shapes created from modified TiO 2 , which might be interpreted as the change in oxidation state, are assessed in terms of relative proportions of titanium to oxygen. It is found through detailed analysis of spectra that quantification by XPS, using procedures routinely used in practice, is not in accord with the typical interpretations of photoemission shapes. The data processing methods used and results presented in this work are of particular relevance to elucidating fundamental phenomena governing the surface evolution of materials-enabled energy processes where cyclic/non-steady usage changes the nature of bonding, especially in the presence of contaminants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗

DOE OSTI2024

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗