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DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Collaborative: Fundamental studies of how water influences the synthesis and behavior of zeolite catalysts for reactions in the circular carbon economy (Final Technical Report)

This project established a computational framework to predict and interpret how synthesis conditions control active site location in MFI zeolites and how those site distributions influence catalytic reactivity. The Hibbitts Group led the theoretical effort, applying periodic DFT calculations to quantify (i) SDA–framework–heteroatom interactions during crystallization and (ii) the energetics and mechanisms of arene methylation reactions within distinct MFI environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Precision cooking system

The present invention comprises a precision cooking oven that utilizes laser sheets to cook food, thus creating homogeneously heated and uniformly seared food bodies and surfaces. Laser sheets move back and forth evenly injecting heat into items being cooked. All food cold or hot areas can be eliminated since intersecting laser lines can be completely projected on and accommodate an item's exterior surfaces. Items with non-uniform cross-sections and properties can be cooked uniformly by controlling the exact amount of energy projected into differing sections of the food. The oven is also capable of cooking autonomously. Since laser sheets can be precisely controlled, cooking results can be very predictable once the boundary conditions for a thermal analysis are determined. The oven can detect the properties of the items to be cooked and thereafter compute the needed time and power to achieve the desired results stipulated by an oven operator.

Lee, Henry Yu-Heng↗

DOE OSTI2025

Control over Banded Morphologies and Circular Dichroism in Chiral Halide Perovskites

Chiral halide perovskites (c-HPs) merge the chirality of organic cations with the semiconducting properties of metal halide frameworks, creating a family of chiral semiconductors with tunable chiroptoelectronic behavior. Here, we describe the impact of periodic banded morphologies of textured c-HP ( R/S -NEA) 2 PbBr 4 films (NEA = 1- (1-naphthyl)ethylammonium) on their chiroptical behavior. Due to the interplay between the crystalline and glassy phases, the c-HP film growth is driven by rhythmic precipitation, producing a distinctive controllable radial banded pattern with the ( R/S -NEA) 2 PbBr 4 inorganic planes oriented parallel to the substrate. The banded morphology can be controlled, as evidenced by the growth temperature dictating the ridge-to-ridge spacing as well as the density of banded regions. The resulting circular dichroism (CD) spectral shape, intensity, and polarity vary in a seemingly random manner across processing conditions. However, these spectral features can be explained by considering key features of the banded morphology, such as refraction of the incident light due to surface morphology, birefringence, and stacked, rotated crystallites. These effects cannot be canceled by averaging front and back CD spectra of c-HP films, and our model incorporating these effects reproduces all observed CD spectra remarkably well. The control over the c-HP morphology and prediction capabilities of our CD modeling leads to further understanding of this class of semiconductors and the possibility of exploiting structural features for light polarization control akin to enhanced metamaterials.

14 SOLAR ENERGY↗

DOE OSTI2021

Resistive wall mode stability and resonant field amplification in MAST high beta plasma

The n=1 (n is the toroidal mode number) resistive wall mode (RWM) instability and the resonant field amplification (RFA) due to a stable RWM response are numerically investigated for a MAST high-pressure plasma scenario, utilizing the MARS-F (Liu Y.Q. et al 2000 Phys. Plasmas 7 3681) and MARS-K (Liu Y.Q. et al 2008 Phys. Plasmas 15 112503) codes, for the purpose of understanding the potential RWM behavior in MAST-U plasmas. Ideal kink stability analysis identifies a target plasma, with parameters similar to that of the reference equilibrium reconstructed from a MAST high-pressure discharge, that accesses the RWM regime. The unstable n=1 RWM for the target equilibrium is subject to strong damping by the plasma toroidal flow and/or the drift kinetic effects from thermal particles. As a result, the mode is found to be stable under the experimental flow conditions, or even without flow stabilization if drift kinetic stabilization is included. The stability prediction is robust against variation of the assumed resistive wall minor radius. Active magneto-hydrodynamic (MHD) spectroscopy modeling, using the magnetic coils designed for controlling the edge localized modes (ELMs) in MAST-U as the antenna, shows strong resonant field amplification due to a stable RWM response in the target plasma. Maximal amplification, of 6-7 times larger than the applied vacuum field within the plasma, is obtained assuming a coil phasing of 120-180 degrees between the upper and lower rows of the ELM control coils. The MHD-kinetic hybrid model predicts generally higher RFA than the fluid model, in particular near the low-field and high-field sides of the plasma boundary surface. Furthermore, the MARS-F/K modeling thus shows that the RWM in MAST-U is subject to strong flow and kinetic stabilization, but can nevertheless be detected by active MHD spectroscopy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Exploring Camelina sativa lipid metabolism regulation by combining gene co-expression and DNA affinity purification analyses

Camelina (Camelina sativa) is an annual oilseed plant that is gaining momentum as a biofuel cover crop. Understanding gene regulatory networks is essential to deciphering plant metabolic pathways, including lipid metabolism. Furthermore, we take advantage of a growing collection of gene expression datasets to predict transcription factors (TFs) associated with the control of Camelina lipid metabolism. We identified approximately 350 TFs highly co-expressed with lipid-related genes (LRGs). These TFs are highly represented in the MYB, AP2/ERF, bZIP, and bHLH families, including a significant number of homologs of well-known Arabidopsis lipid and seed developmental regulators. After prioritizing the top 22 TFs for further validation, we identified DNA-binding sites and predicted target genes for 16 out of the 22 TFs tested using DNA affinity purification followed by sequencing (DAP-seq). Enrichment analyses of targets supported the co-expression prediction for most TF candidates, and the comparison to Arabidopsis revealed some common themes, but also aspects unique to Camelina. Within the top potential lipid regulators, we identified CsaMYB1, CsaABI3AVP1-2, CsaHB1, CsaNAC2, CsaMYB3, and CsaNAC1 as likely involved in the control of seed fatty acid elongation and CsaABI3AVP1-2 and CsabZIP1 as potential regulators of the synthesis and degradation of triacylglycerols (TAGs), respectively. Altogether, the integration of co-expression data and DNA-binding assays permitted us to generate a high-confidence and short list of Camelina TFs involved in the control of lipid metabolism during seed development.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Prediction of histone post-translational modifications using deep learning

Abstract Motivation Histone post-translational modifications (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence. Results In this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type-specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data. Availabilityand implementation https://github.com/dDipankar/DeepPTM. Supplementary information Supplementary data are available at Bioinformatics online.

Baisya, Dipankar Ranjan (ORCID:0000000267847359)↗

DOE OSTI2025

Reinforcement Learning‐Based Adaptation of Grid Following Inverter's Internal Controller to Networked Microgrids' Strengths

The varying topological configurations, generator commitments and dispatches, and dynamic load demand lead to changing system's strengths during the operations of networked microgrids. When the system's strengths significantly change, the fixed control gains at large devices may result in unsatisfactory system performance; this necessitates the tuning of the control gains at large devices to adapt to the changing system's strengths. In this paper, observer-based reinforcement learning (RL) is utilised to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing strengths of microgrids and networked microgrids. The RL agent in this framework augments an observer predicting system's strengths, from which the RL control policy will adjust accordingly to tune the PLL controller's gains towards the system's strengths. Also, to enhance the control performance, the recently introduced Barrier function-based RL framework is leveraged for the design of reward function to prevent the high frequency nadir. An operational 26 kV electric distribution system, which is modelled as networked microgrids, is used to illustrate the need and effectiveness of the proposed RL-tuned control.

frequency response↗

DOE OSTI2022

Designing Magnetism in High Entropy Oxides

Abstract In magnetic systems, spin and exchange disorder can provide access to quantum criticality, frustration, and spin dynamics, but broad tunability of these responses and a deeper understanding of strong limit disorder are lacking. Here, it is demonstrated that high entropy oxides present a previously unexplored route to designing materials in which the presence of strong local compositional disorder may be exploited to generate tunable magnetic behaviors—from macroscopically ordered states to frustration‐driven dynamic spin interactions. Single‐crystal La(Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 films are used as a model system hosting a magnetic sublattice with a high degree of microstate disorder in the form of site‐to‐site spin and exchange type inhomogeneity. A classical Heisenberg model simplified to represent the highest probability microstates well describes how compositionally disordered systems can paradoxically host magnetic uniformity and demonstrates a path toward continuous control over ordering types and critical temperatures. Model‐predicted materials are synthesized and found to possess an incipient quantum critical point when magnetic ordering types are designed to be in direct competition, this leads to highly controllable exchange bias behaviors previously accessible only in intentionally designed bilayer heterojunctions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Key predictors of soil organic matter vulnerability to mineralization differ with depth at a continental scale

Abstract Soil organic matter (SOM) is the largest terrestrial pool of organic carbon, and potential carbon-climate feedbacks involving SOM decomposition could exacerbate anthropogenic climate change. However, our understanding of the controls on SOM mineralization is still incomplete, and as such, our ability to predict carbon-climate feedbacks is limited. To improve our understanding of controls on SOM decomposition, A and upper B horizon soil samples from 26 National Ecological Observatory Network (NEON) sites spanning the conterminous U.S. were incubated for 52 weeks under conditions representing site-specific mean summer temperature and sample-specific field capacity (−33 kPa) water potential. Cumulative carbon dioxide respired was periodically measured and normalized by soil organic C content to calculate cumulative specific respiration (CSR), a metric of SOM vulnerability to mineralization. The Boruta algorithm, a feature selection algorithm, was used to select important predictors of CSR from 159 variables. A diverse suite of predictors was selected (12 for A horizons, 7 for B horizons) with predictors falling into three categories corresponding to SOM chemistry, reactive Fe and Al phases, and site moisture availability. The relationship between SOM chemistry predictors and CSR was complex, while sites that had greater concentrations of reactive Fe and Al phases or were wetter had lower CSR. Only three predictors were selected for both horizon types, suggesting dominant controls on SOM decomposition differ by horizon. Our findings contribute to the emerging consensus that a broad array of controls regulates SOM decomposition at large scales and highlight the need to consider changing controls with depth.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Multidimensional coherent spectroscopy of light-driven states and their collective modes in multiband superconductors

We present a comprehensive theory of light-controlled multiband superconductivity, and apply it to predict distinctive signatures of light-driven superconducting (SC) states in terahertz multidimensional coherent spectroscopy (THz-MDCS) experiments. We first derive gauge-invariant Maxwell-Bloch equations for multiband BCS superconductors with spatial fluctuations. We consider driving electromagnetic fields determined self-consistently by Maxwell's equations. By calculating the THz-MDCS spectra measured experimentally in the clean SC limit, we identify unique signatures of finite-momentum Cooper-pairing states that live longer than the laser pulse. They are controlled by a pair of THz laser pulses with well-defined relative phase (pulse pair). The pseudospin oscillators that describe the properties of these SC states are parametrically driven by both finite-momentum Cooper pairing and by time oscillations of the order-parameter relative phase. We show that such strong parametric driving leads to drastic changes in the THz-MDCS spectral shape from the predictions of third-order nonlinear susceptibility calculations. These spectral changes strongly depend on the interband-to-intraband interaction ratio and on the collective modes of the light-driven state. For negligible interband interaction, the spectra show a transition with increasing field, from traditional pump-probe, four-wave-mixing, and third-harmonic generation peaks determined by the laser frequency to sidebands determined by the excitations of the driven system. These sidebands emerge from difference-frequency Raman processes in the nonequilibrium SC state. For interband couplings weaker than the intraband pairing, we show that the Leggett phase collective mode leads to harmonic sidebands around the traditional pump-probe peaks. Additional Higgs collective mode peaks result from light-induced inversion-symmetry breaking in a thin-film geometry. For strong interband coupling, we find a transition from a nonequilibrium finite Cooper-pair momentum state characterized by hybrid-Higgs amplitude mode peaks in THz-MDCS spectra to a driven state identified experimentally by the emergence of Floquet-type sidebands at bi-Higgs frequencies. Those dominant bi-Higgs-frequency satellites are manifestations of a new order parameter relative phase collective mode that characterizes the nonequilibrium SC state. The predicted interaction- and field-dependent transitions in the spectral profile allow us to propose THz-MDCS experiments for quantum tomography of light-driven superconductivity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Large Divergence of Projected High Latitude Vegetation Composition and Productivity Due To Functional Trait Uncertainty

Abstract Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM‐FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold‐induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Toward non-Si electronics: From remote epitaxy to layer splitting of 2D materials for mixed dimensional heterostructures

The current electronics has been mainly dominated by Si-based devices due to their mature processing system and exceptional cost-effectiveness. However, next generation electronics needs novel functionalities that cannot be realized by Si because of intrinsic limitation of Si. Accordingly, demand for non-Si electronics has been getting substantially high. Unfortunately, current methodology requires extremely high cost for non-Si materials, which impedes the progress in developing the non-Si based electronics. Here, I will discuss about our group’s efforts to address this issue. Our team recently conceived a new crystalline growth, termed as “remote epitaxy”, which can copy/paste crystalline information from substrates remotely through graphene, thus generating single-crystalline films on graphene. As interfacial binding energy is attenuated by inserting graphene at interface, the single-crystalline films can be easily exfoliated from the slippery graphene surface. Also, the graphene-coated substrates can be, in principle, reused infinitely to produce single-crystalline films. Thus, the remote epitaxy can produce non-Si semiconductor films with unprecedented cost efficiency while allowing additional flexible device functionality required for current ubiquitous electronics. Next, I will discuss about a layer splitting technique which can be a potential solution to overcome the problem in obtaining large-scale and monolayer 2D materials. A 2D material-based heterostructure has been intensively studied because of its unique device functionalities and novel physics. However, it is extremely challenging to secure large-scale and monolayer 2D materials because of following issues: 1) poor scalability for laboratory fabrication processes of 2D heterostructures and 2) lack of well-defined control parameters for kinetics of 2D materials and predictable number of layers of 2D materials. To resolve this issue, we conceived a new approach called “layer-resolved splitting” which obtains multiple monolayer from multilayer 2D materials by controlling interfacial toughness contrast. As this method is versatile and universal, we can, in principle, apply to all 2D materials. We succeeded in having large-scale, monolayer 2D materials through our approach and, thereby 2D heterostructures were demonstrated for functional devices. Lastly, I would like to discuss opportunities of mixed-dimensional heterostructure demonstrated by remote epitaxy and layer-resolved splitting. As they produce freestanding 3D bulk films and 2D atomic layers, a new type of 3D/2D heterostructures can be realized where a new physics and new device architecture are revealed. Therefore, I believe that a new opportunity will be discovered through the mixed-dimensional heterostructures.

Bae, Sang-Hoon↗

DOE OSTI2024

Rational Design of Novel Biomimetic Sequence-Defined Polymers for Mineralization Applications

Silica biomineralization is a naturally occurring process, wherein organisms use proteins and other biological structures to direct the formation of complex, hierarchical nanostructures. Discovery and characterization of such proteins and their underlying mechanisms spurred significant efforts to identify routes for biomimetic mineralization that reproduce the exquisite shapes and size selectivities found in nature. A common strategy has been the use of short peptide sequences with chemistry mimicking those found in natural systems, such as the use of the silaffin-derived R5 peptide. While progress has been made using this approach, there are many limitations that have prevented breakthroughs in biomimicry. To advance our ability to use charged macromolecules for silica formation, we propose to use sequence-defined synthetic polymers known as peptoids, or N-substituted polyglycines, which present significant capability for the precise tuning of sequence and structure beyond what can often be achieved with peptides alone. This study presents a computationally predicted design of these polymers that leads to the controlled formation of silica nanomaterials. We investigate surface adsorption and the mineralization process through analysis of binding mechanisms and energetics of the R5 system. Next, we synthesized two R5-inspired peptoids and validated our prediction in the design of mineralization polymers through characterization using surface plasmon resonance and electron microscopy. Here, this computationally guided study holds great promise for designing new sequences with unprecedented control of the placement of chemical functional groups, thus allowing for further unraveling of silicification mechanisms and the eventual design of sequence-defined synthetic polymers leading to the predictive synthesis of nanostructured functional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Deterministic incident-wave elevation prediction in intermediate water depth

Potential performance gains from optimal (non-causal) impedance-matching control of wave energy devices in irregular ocean waves are dependent on deterministic wave elevation prediction techniques that work well in practical applications. Although a number of devices are designed for operation in intermediate water depths, little work has been reported on deterministic wave prediction in such depths. Here, we investigated a deterministic wave-prediction technique based on an approximate propagation model that leads to an analytical formulation, which may be convenient to implement in practice. To improve accuracy, an approach to combine predictions based on multiple up-wave measurement points is evaluated. The overall method is tested using experimental time-series measurements recorded in the U.S. Navy MASK basin in Carderock, MD, USA. For comparison, an alternative prediction approach based on Fourier coefficients is also tested with the same data. Comparison of prediction approaches with direct measurements suggest room for improvement. Possible sources of error including tank reflections are estimated, and potential mitigation approaches are discussed.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Influence of ontogenetic development, temperature, and pCO2 on otolith calcium carbonate polymorph composition in sturgeons

Changes to calcium carbonate (CaCO 3 ) biomineralization in aquatic organisms is among the many predicted effects of climate change. Because otolith (hearing/orientation structures in fish) CaCO 3 precipitation and polymorph composition are controlled by genetic and environmental factors, climate change may be predicted to affect the phenotypic plasticity of otoliths. We examined precipitation of otolith polymorphs (aragonite, vaterite, calcite) during early life history in two species of sturgeon, Lake Sturgeon, (Acipenser fulvescens) and White Sturgeon (A. transmontanus), using quantitative X-ray microdiffraction. Both species showed similar fluctuations in otolith polymorphs with a significant shift in the proportions of vaterite and aragonite in sagittal otoliths coinciding with the transition to fully exogenous feeding. We also examined the effect of the environment on otolith morphology and polymorph composition during early life history in Lake Sturgeon larvae reared in varying temperature (16/22 °C) and pCO 2 (1000/2500 µatm) environments for 5 months. Fish raised in elevated temperature had significantly increased otolith size and precipitation of large single calcite crystals. Interestingly, pCO 2 had no statistically significant effect on size or polymorph composition of otoliths despite blood pH exhibiting a mild alkalosis, which is contrary to what has been observed in several studies on marine fishes. These results suggest climate change may influence otolith polymorph composition during early life history in Lake Sturgeon.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗