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All sources NASA NTRS DOE OSTI

DOE OSTI2018

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 10 4 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

36 MATERIALS SCIENCE↗

DOE OSTI2024

R ( 3780 ) Resonance Interpreted as the 1 D 1 3 -Wave Dominant State of Charmonium from Precise Measurements of the Cross Section of e + e − → Hadrons

We report the precise measurements of the cross section of e + e − → hadrons at center-of-mass energies from 3.645 to 3.871 GeV. We thereby perform the most precise study of the cross sections and find a complex system composed of three resonances of R ( 3760 ) , R ( 3780 ) , and R ( 3810 ) . For the first time, we measure the R ( 3810 ) electronic width to be ( 19.4 ± 7.4 ± 12.1 ) eV . For the R ( 3760 ) resonance, we measure the mass to be ( 3751.9 ± 3.8 ± 2.8 ) MeV / c 2 , the total width to be ( 32.8 ± 5.8 ± 8.7 ) MeV , and the electronic width to be ( 184 ± 75 ± 86 ) eV . For the R ( 3780 ) resonance, we measure its mass to be ( 3778.7 ± 0.5 ± 0.3 ) MeV / c 2 , total width to be ( 20.3 ± 0.8 ± 1.7 ) MeV , and electronic width to be ( 265 ± 67 ± 83 ) eV . Forty-seven years ago, the ψ ( 3770 ) resonance was discovered, and was subsequently interpreted as the 1 D 1 3 -wave dominant state of charmonium. However, our analysis of the total-hadron cross sections indicates that the ψ ( 3770 ) is not a single state, but a complex system composed of the R ( 3760 ) , R ( 3780 ) , and R ( 3810 ) resonances. Among these, we interpret the R ( 3780 ) is a resonance dominated by the 1 D 1 3 charmonium state. Published by the American Physical Society 2024

Ablikim, M.↗

DOE OSTI2021

Methodology for interpretable reinforcement learning model for HVAC energy control

Deep reinforcement learning (DRL) approaches have been used in various application areas to improve efficiency, optimization, or automation. However, very little is known about how the DRL algorithms make decisions and what features affect their performance. Using a case study of a DRL based Heating, Ventilation and Air Conditioning (HVAC) optimization methodology, we demonstrate how we can address these challenges by applying interpretability tools and systematically exploring the model inputs for better understanding the DRL behaviour and decision making process. We developed a methodology for interpretable reinforcement learning and evaluated our approach in real-world house located in Knoxville, TN. Our findings explain the reasoning behind DRL-based optimization decisions under different circumstances which has been discussed and confirmed by the experts in the field.

Kotevska, Olivera↗

DOE OSTI2025

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗

DOE OSTI2020

Automating the interpretation of PM 2.5 time–resolved measurements using a data–driven approach

The rapid development of automated measurement equipment enables researchers to collect greater quantities of time-resolved data from indoor and outdoor environments. While significant, the interpretation of the resulting data can be a time-consuming effort. This paper introduces an automated process of interpreting PM 2.5 time-resolved data and differentiating PM 2.5 emissions resulting from indoor and outdoor sources. Here, we use Random Forest (RF), a machine learning approach, to study a dataset of 836 indoor emission events that occurred over a 2-week period in 18 apartments in California. In this paper, we show model development and evaluate its performance as the sample size and source vary. We discuss the characteristics of the dataset that tended to help the source identification and why. For example, we show that data from many events and from different apartments are essential for the model to be suitable for analyzing a new separate dataset. We also show that longitudinal data appear to be more helpful than the time frequency of measurements within a given apartment. We use the resulting RF model to analyze PM 2.5 data of an entirely separate dataset collected from 65 new homes in California. The RF model identifies 442 indoor emission events, with only a few misidentifications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Tutorial on interpreting x-ray photoelectron spectroscopy survey spectra: Questions and answers on spectra from the atomic layer deposition of Al 2 O 3 on silicon

X-ray photoelectron spectroscopy (XPS) has become the most widely used method for chemically analyzing surfaces. In XPS, photoelectrons are generated by irradiating a surface with X-rays. As the importance and popularity of XPS have grown, it has drawn users without significant XPS experience, and incorrect and incomplete interpretations of XPS spectra regularly appear in the literature. This tutorial is designed as a tool to guide less experienced users in analyzing XPS survey spectra. Here, we examine a series of XPS survey spectra collected during the atomic layer deposition of Al2O3 from trimethylaluminum and water precursors. Prior to this, brief explanations of XPS and atomic layer deposition (ALD) are presented. This tutorial is structured as a series of questions and answers that the interested reader may choose to engage in. The XPS spectra are scrutinized to extract information about the elements present in the film, the presence of contamination, and the nature of the film growth process. The questions and answers in this tutorial address important fundamental issues common to the interpretation of many XPS survey spectra in the context of ALD.

XPS spectrum, Tutorial, Atomic layer deposition↗

DOE OSTI2013

Dose equations for tube current modulation in CT scanning and the interpretation of the associated CTDI{sub vol}

Purpose: The scanner-reported CTDI{sub vol} for automatic tube current modulation (TCM) has a different physical meaning from the traditional CTDI{sub vol} at constant mA, resulting in the dichotomy “CTDI{sub vol} of the first and second kinds” for which a physical interpretation is sought in hopes of establishing some commonality between the two.Methods: Rigorous equations are derived to describe the accumulated dose distributions for TCM. A comparison with formulae for scanner-reported CTDI{sub vol} clearly identifies the source of their differences. Graphical dose simulations are also provided for a variety of TCM tube current distributions (including constant mA), all having the same scanner-reported CTDI{sub vol}.Results: These convolution equations and simulations show that the local dose at z depends only weakly on the local tube current i(z) due to the strong influence of scatter from all other locations along z, and that the “local CTDI{sub vol}(z)” does not represent a local dose but rather only a relative i(z) ≡ mA(z). TCM is a shift-variant technique to which the CTDI-paradigm does not apply and its application to TCM leads to a CTDI{sub vol} of the second kind which lacks relevance.Conclusions: While the traditional CTDI{sub vol} at constant mA conveys useful information (the peak dose at the center of the scan length), CTDI{sub vol} of the second kind conveys no useful information about the associated TCM dose distribution it purportedly represents and its physical interpretation remains elusive. On the other hand, the total energy absorbed E (“integral dose”) as well as its surrogate DLP remain robust between variable i(z) TCM and constant current i{sub 0} techniques, both depending only on the total mAs = t{sub 0}=i{sub 0} t{sub 0} during the beam-on time t{sub 0}.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

On interpretation of fluctuations of conserved charges at high T

Abstract Fluctuations of conserved charges calculated on the lattice which can be measured experimentally, are well reproduced by a hadron resonanse gas model at temperatures below$$T_{ch} \sim 155$$ T ch ∼ 155 MeV and radically deviate from the hadron resonance gas predictions above the chiral restoration crossover. This behaviour is typically interpreted as an indication of deconfinement in the quark-gluon plasma regime. We present an argument that this interpretation may be too simple. The argument is based on the scaling of quantities with the number of colors: demonstration of deconfinement and QGP requires observable that is sensitive to$$\sim N_c^2$$ ∼ N c 2 gluons while the conserved charges are sensitive only to quarks and above$$T_{ch}$$ T ch scale as$$N_c^1$$ N c 1 . The latter scaling is consistent with the existence of an intermediate regime characterized by restored chiral symmetry and by approximate chiral spin symmetry which is a symmetry of confining interaction. In this regime the energy density, pressure and entropy density scale as$$N_c^1$$ N c 1 . In the large$$N_c$$ N c limit this regime might become a distinct phase separated from the hadron gas and from QGP by phase transitions. A natural observable that associates with deconfinement and is directly sensitive to deconfined$$N_c^2-1$$ N c 2 - 1 gluons is the Polyakov loop; in the$$N_c=3$$ N c = 3 world it remains very close to 0 at temperatures well above chiral crossover, reaches the value$$\sim 0.5$$ ∼ 0.5 around$$\sim 3T_{ch}$$ ∼ 3 T ch and the value close to 1 at temperatures$$\sim 1$$ ∼ 1 GeV.

Physics↗

DOE OSTI2025

Measurement of high-mass $t\bar{t}\ell ^{+}\ell ^{-}$ production and lepton flavour universality-inspired effective field theory interpretations at $\sqrt{s}=13$ $\text {T}\text {e}\hspace{-1.00006pt}\text {V}$ with the ATLAS detector

Measurements of $t\bar{t}\ell ^{+}\ell ^{-}$ production in the region of high dilepton invariant mass with effective field theory (EFT) interpretations are presented. They are performed using final states with three isolated leptons (electrons or muons) and are based on $\sqrt{s} = 13$ TeV proton–proton collision data with an integrated luminosity of $140\,\textrm{fb}^{-1}$, recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider. Measurements of the $t\bar{t}\ell ^{+}\ell ^{-}$ signal strength and cross-section upper-limits are performed inclusively in lepton flavour and separately for electrons and muons. The study also aims to probe anomalous four-fermion interactions including to test for possible lepton flavor universality violation. No significant deviations from the Standard Model predictions are observed and the measurements are interpreted through the EFT formalism to provide new constraints on the relevant operators.

Aad, G. [CNRS/IN2P3] (ORCID:0000000266654934)↗

DOE OSTI2025

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗

DOE OSTI2025

Physical Interpretation of Early Battery Life Prediction Models

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.

25 ENERGY STORAGE↗

DOE OSTI2023

Feature Interpretability

The feature interpretability code is a python module that interprets and analyzes neural networks trained on hydrodynamic simulation output in the form of numpy arrays. The code takes trained neural networks and extracts internal model states in the form of images. Additionally, tools for covariance analysis of network weights and predictions are provided. This code is built on the TensorFlow and PyTorch python libraries, and includes trained networks and example input data for demonstration purposes.

Callis, Skylar↗

DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Unified Interpretation of Variability in Precipitation Isotope Ratios

Abstract Several mechanisms have been proposed to explain why the isotope ratios of precipitation vary in space and time and why they correlate with other climate variables like temperature and precipitation. Here, we argue that this behavior is best understood through the lens of radiative transfer, which treats the depletion of atmospheric vapor transport by precipitation as analogous to the attenuation of light by absorption or scattering. Building on earlier work by Siler et al., we introduce a simple model that uses the equations of radiative transfer to approximate the two-dimensional pattern of the oxygen isotope composition of precipitation ( δ p ) from monthly mean hydrologic variables. The model accurately simulates the spatial and seasonal variability in δ p within a state-of-the-art climate model and permits a simple decomposition of δ p variability into contributions from gradients in evaporation and the length scale of vapor transport. Outside the tropics, δ p is mostly controlled by gradients in evaporation, whose dependence on temperature explains the positive correlation between δ p and temperature (i.e., the temperature effect). At low latitudes, δ p is mostly controlled by gradients in the transport length scale, whose inverse relationship with precipitation explains the negative correlation between δ p and precipitation (i.e., the amount effect). This suggests that the temperature and amount effects are both mostly explained by the variability in upstream rainout, but they reflect distinct mechanisms governing rainout at different latitudes. Significance Statement The isotopic composition of precipitation has long been used to make inferences about past climates based on its observed relationship with precipitation in the tropics and with temperature at higher latitudes. These relationships—known as the “amount effect” and “temperature effect,” respectively—have been attributed to many different mechanisms, most of which are thought to operate at either high or low latitudes but not both. Here, we present a unified framework for interpreting the isotope variability that can explain the latitude dependence of the temperature and amount effects despite making no distinction between high and low latitudes. Although our results are generally consistent with certain interpretations of the amount effect, they suggest that the temperature effect is widely misunderstood.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗

DOE OSTI2018

Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil , to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. In this paper, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Making Phase-Picking Neural Networks More Consistent and Interpretable

Improving the interpretability of phase-picking neural networks remains an important task to facilitate their deployment to routine, real-time seismic monitoring. The popular phase-picking neural networks published in the literature lack interpretability because their output prediction scores do not necessarily correspond with the reliability of phase picks and can even be highly inconsistent depending on how we window the waveform data. Here, we show that systematically shifting the waveforms during training and using an antialiasing filter within the neural network architecture can substantially improve the consistency of the output prediction scores and can even make them scale with the signal-to-noise ratios of the waveforms. We demonstrate the improvements by applying these approaches to a commonly used phase-picking neural network architecture and using waveform data from the 2019 Ridgecrest earthquake sequence.

58 GEOSCIENCES↗

DOE OSTI2020

Interpreting pre-vegetation landscape dynamics: The Cambrian Lower Mount Simon Sandstone, Illinois, U.S.A.

ABSTRACT The Cambrian Mount Simon Sandstone has been the subject of extensive study and multiple industrial-scale carbon storage demonstrations at Decatur, Illinois, USA. The development of a reliable paleoenvironmental model is critical to successful large-scale carbon dioxide (CO2) storage, but is complicated by the need to interpret pre-vegetation sedimentation processes. The present study presents a paleoenvironmental model of the Lower Mount Simon Sandstone, based on analysis of primary sedimentary structures in two cores and four complete high-resolution resistivity logs (FMI). The Lower Mount Simon Sandstone represents a vertical “drying-up” sequence composed of three associated depositional units: a north–south oriented coastal system at the base, an eastward-directed fluvial unit in the middle, and a westward-directed eolian system at the top that recycled medium- and fine-grained sand in the basin. Quantitative analysis of fluvial cross-strata indicates that the perennial river system was shallow (c. 1 m deep) with relatively narrow channel belts (c. 1 km). Adjacent sandy eolian-floodplain deposits contain abundant thin, crinkly planar laminae that are enriched in fines and are interpreted as cementation surfaces, likely of biological origin. Deflation lags and wind-ripple strata are commonly interbedded with the crinkly strata, suggesting that the recurrence of erosion and deposition that controlled sedimentary preservation on the floodplain were dominated by eolian transport, re-wetting, and (bio-) cementation. Such a prominent role of exposure to the wind, basin-scale sediment recycling, and eolian removal of fine-grained sediment would have ceased to exist for most climates after the development of vegetation on land, yet, may well be key to understanding the environmental context for early life on Earth.

Geology↗