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DOE OSTI2024

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Learning-based Power Management Method for Networked Microgrids Under Incomplete Information

In this paper, we present a model-free reinforcement learning (RL)-based technique to solve the power management problem of networked microgrids (MGs), when the utility has only limited or no detailed knowledge of the MG asset models.The proposed method consists of two levels: at the first level, using an approximate RL strategy and aggregated data, a utility agent determines the locational price signals to maximize its profit under incomplete information on the MGs’ private data behind their points of common coupling (PCC). The proposed RL strategy enables the utility to adapt to changing system conditions and learn from previous experiences, while respecting the data ownership of the MGs. At the second level, the MGs receive the price signals to dispatch their generation/storage assets individually over look-ahead decision time window, while considering power flow constraints. The performance of the proposed RL technique has been verified under realistic operational scenarios.

Zhang, Qianzhi↗

DOE OSTI2020

Systems-level investigation of aqueous batteries for understanding the benefit of water-in-salt electrolyte by synchrotron nanoimaging

Water-in-salt (WIS) electrolytes provide a promising path toward aqueous battery systems with enlarged operating voltage windows for better safety and environmental sustainability. In this work, a new electrode couple, LiV 3 O 8 -LiMn 2 O 4 , for aqueous Li-ion batteries is investigated to understand the mechanism by which the WIS electrolyte improves the cycling stability at an extended voltage window. Operando synchrotron transmission x-ray microscopy on the LiMn 2 O 4 cathode reveals that the WIS electrolyte suppresses the mechanical damage to the electrode network and dissolution of the electrode particles, in addition to delaying the water decomposition process. Because the viscosity of WIS is notably higher, the reaction heterogeneity of the electrodes is quantified with x-ray absorption spectroscopic imaging, visualizing the kinetic limitations of the WIS electrolyte. This work furthers the mechanistic understanding of electrode–WIS electrolyte interactions and paves the way to explore the strategy to mitigate their possible kinetic limitations in three-dimensional architectures.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints

We report a recurrent neural network (RNN) based model is developed as a surrogate to predict nonlinear plastic response under multiaxial loading. The RNN-based model is trained and tested on stress versus strain curves generated using a numerical solution based on the classical radial return method. Besides simply learning the basic constitutive relationship, a novel approach is taken to enforce certain physical conditions. Specifically, regularization is employed to maintain non-negative plastic power density throughout the loading history thereby ensuring monotonically increasing plastic work and thermodynamic consistency. Enforcing physics in this manner permits coupling of the data-driven RNN approach with physics-based knowledge and laws. This has the effect of reducing the necessary amount of data and ensuring known physical laws are not violated. Since, once trained, the model need not perform the expensive task of solving nonlinear equations, its efficiency is orders of magnitude greater than its numerical counterpart. The RNN-based model has been trained on varied sets of data and the accuracy on test datasets validated. The developed model is general and robust and has widespread application such as in the simulation of metal forming, large scale plasticity, and part life prediction.

42 ENGINEERING↗

DOE OSTI2023

Coupling Shared E-scooters and Public Transit: A Spatial and Temporal Analysis

The integration of shared e-scooters with public transit is a promising solution for urban mobility's first/last-mile challenge. This study explores spatiotemporal factors influencing this integration, using 35-day e-scooter trip data from Chicago. Employing a random-effect negative binomial approach, we modeled the frequency of e-scooter trips to access/egress to/from bus stops and train stations. Results indicate that weather conditions, design features like intersection density, and multimodal network density significantly influence usage. The transit system characteristics such as service frequency have a positive effect on the integration of e-scooters and trains while a similar effect for bus and e-scooter integration was not significant. Furthermore, safety-related variables such as accident and crime rates as well as demographic characteristics were also revealed to be significant factors in our study. These findings offer vital insights to urban planners and policymakers for infrastructure, safety enhancements, and interventions to encourage efficient e-scooter-public transit integration.

Chicago↗

DOE OSTI2018

Active Quenching Technique for YBCO Tapes: Quench Acceleration and Protection

The application of resistive-type superconducting fault current limiters (RSFCLs) in electrical networks is very attractive due to their relative compactness, light weight, and good performance. However, this technology still has drawbacks: asymmetrical quench, uncertain limiting velocity, passive action and incapability of enlarging capacity. Here, we present an active quenching technique which can potentially solve these problems. The quenching process is triggered using high-frequency (HF) AC fields, which are generated by two coupled copper coils attached on both sides of the YBCO sample. Our experiments show that this approach is effective in guaranteeing uniform quench and therefore is expected to be able to significantly extend the service life of the device. Moreover, the quench speed can be considerably increased by the HF field. We find that the performance of acceleration is positively correlated to the transport current, intensity and frequency of the AC field. In addition, a DC magnetic field is added around the sample holder simultaneously with the AC field, to study the field-angular-frequency dependence of the quench time t(B{sub AC}f{sub AC}B{sub DC}𝜃{sub DC}). Experimental results prove that the DC magnetic field can cooperate with the HF AC field to accelerate quench, which means better performance can be produced with lower costs with the two fields acting together. In all, this technique showed outstanding performance regarding quench acceleration and tape protection. We believe the HF-assisted quenching technology has a promising future in current-limiting devices and hope our findings could be helpful for its potential applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Earthquake Damage Patterns Resolve Complex Rupture Processes

Fracture damage patterns around faults induced by dynamic earthquake rupture are an invaluable record to clarify the rupture process on complex fault networks. The 2016 M w 7.8 Kaikōura earthquake in New Zealand has been reported as one of the most complex earthquakes ever documented that ruptured at least 15 crustal faults. High-resolution optical satellite image displacement maps provide distinctive profiles of displacement across the faults and help visualize the off-fault damage pattern. They are combined with field observation and coupled with a numerical tool that captures the dynamics of the rupture and simultaneous activation of off-fault damage to allow the determination of the most likely rupture scenario. Lastly, this study demonstrates that complex rupture processes can be explained in a rather simple way via a synergetic combination of state-of-the-art observation and first principle physics-based numerical modeling of off-fault damage.

58 GEOSCIENCES↗

DOE OSTI2026

A dendritic strontium river isoscape for fisheries applications in the Sacramento River basin, California, USA

Objective Understanding the origins and movements of fish is fundamental to effective conservation and fisheries management. Strontium isotope ratios ( 87 Sr/ 86 Sr) in otoliths provide a powerful tracer of natal origin and migratory pathways. However, existing 87 Sr/ 86 Sr isoscapes for the Sacramento River basin, an ecosystem that supports ecologically and economically important salmon populations, rely on discrete classification approaches that overlook unsampled habitats and do not incorporate spatial uncertainty. Our objective was to develop a continuous, network-explicit 87 Sr/ 86 Sr isoscape with quantified uncertainty to fill in data gaps and enable probabilistic assignments of fish origin and movement. Methods We used river water 87 Sr/ 86 Sr data from 106 sites (1997–2021) to develop spatial stream network models that use dendritic connectivity and watershed characteristics (lithology, bedrock age, and land cover) to predict river water 87 Sr/ 86 Sr throughout the basin. Models were fitted using maximum and restricted likelihood and were evaluated via Akaike’s information criterion and leave-one-out cross validation. We produced both historical (pre-dam) and present-day (below-dam) isoscapes, delineated uncertainty-informed isotopic ranges using k -means clustering, and applied a proof-of-concept Bayesian assignment to estimate natal origins and early rearing habitats for two endangered winter-run Chinook Salmon Oncorhynchus tshawytscha. Results Cross validation indicated strong performance of the 87 Sr/ 86 Sr model (leave-one-out cross validation: R 2 = 0.91; root mean square error = 0.0005). Uncertainty-informed clustering identified 19 isotopic “suites” (reaches with indistinguishable 87 Sr/ 86 Sr values) in present-day anadromous habitats and 25 suites in the historical network. Example natal and early rearing assignments included predictions that challenged expectations for juvenile salmon migration based on predicted river 87 Sr/ 86 Sr compositions. Conclusions This study developed a continuous, network-explicit 87 Sr/ 86 Sr isoscape that integrates existing river data to predict 87 Sr/ 86 Sr in unsampled reaches and the likely achievable range and resolution of otolith-based origin and life history inference. The resulting river isoscape provides a valuable tool to predict salmon movements and identify habitats supporting their survival and growth that otherwise might remain undetected. Coupling these predictions with complementary approaches that ground-truth juvenile presence (e.g., targeted fish surveys) represents an important step toward science-informed restoration and management of critical habitats throughout the Sacramento River basin.

Environmental sciences↗

DOE OSTI2023

Optimal mass variables for semivisible jets

Strongly coupled hidden sector theories predict collider production of invisible, composite dark matter candidates mixed with standard model hadrons in the form of semivisible jets. Classical mass reconstruction techniques may not be optimal for these unusual topologies, in which the missing transverse momentum comes from massive particles and has a nontrivial relationship to the visible jet momentum. We apply the artificial event variable network, a semisupervised, interpretable machine learning technique that uses an information bottleneck, to derive superior mass reconstruction functions for several cases of resonant semivisible jet production. We demonstrate that the technique can extrapolate to unknown signal model parameter values. We further demonstrate the viability of conducting an actual search for new physics using this method, by applying the learned functions to standard model background events from quantum chromodynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments

Abstract. Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments. It aims to optimize the radar observation strategy for deep convection through the use of high-spatiotemporal cloud-resolving model simulations, which resolve the evolution of individual convective cells every 1 min, coupled with a radar simulator and a cell tracking algorithm. The radar simulation settings are based on the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field campaigns held in the Houston, TX, area but are generalizable to other field campaigns focusing on isolated deep convection. Our analysis produces the following four outcomes. First, a 5–7 m s−1 median difference in maximum updrafts of tracked cells is shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s−1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single-RHI (range height indicator) updraft retrieval technique for cells close to the radars, for which multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s−1.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Decoding THz‐Driven Dynamic Fingerprints of Ferroelectric Nanotwin Networks

Ultrafast polarization dynamics in ferroelectrics are of considerable interest for high-speed tunable dielectrics and electro-optics. Extended domain wall networks formed in ferroelectric twin nanodomains can support collective dynamics in the terahertz regime but require techniques that track polarization and strain evolution driven by ultrafast stimulus. Here, we use multi-modal probing of THz-pulse-driven excitations in PbTiO 3 /SrTiO 3 superlattices by combining X-ray free electron laser measurements that directly tracks lattice changes, with optical second harmonic generation that tracks the electronic potential coupled with the lattice potential. Dynamical phase-field modeling enables fingerprinting of these collective modes as superpositions of domain “breathing” through wall oscillations and polarization “rotations” with still walls. Ultrafast domain wall motion at 0.1–0.5 THz is observed at practical fields of 100 kV/cm with wall velocities of >4000 m/s, approaching typical speed of sound in PbTiO 3 . A unique “charging” mode is discovered that can electrically charge and discharge domain walls on ∼4 ps time scale thus dynamically tuning wall conductivity. Integrated experimental and theoretical fingerprinting of the dynamical landscape presented here enables ultrafast control of ferroics for high-speed microelectronics and optical applications.

THz dynamics↗

DOE OSTI2024

Embedded symmetric positive semi-definite machine-learned elements for reduced-order modeling in finite-element simulations with application to threaded fasteners

Here, we present a machine-learning strategy for finite element analysis of solid mechanics wherein we replace complex portions of a computational domain with a data-driven surrogate. In the proposed strategy, we decompose a computational domain into an “outer” coarse-scale domain that we resolve using a finite element method (FEM) and an “inner” fine-scale domain. We then develop a machine-learned (ML) model for the impact of the inner domain on the outer domain. In essence, for solid mechanics, our machine-learned surrogate performs static condensation of the inner domain degrees of freedom. This is achieved by learning the map from displacements on the inner-outer domain interface boundary to forces contributed by the inner domain to the outer domain on the same interface boundary. We consider two such mappings, one that directly maps from displacements to forces without constraints, and one that maps from displacements to forces by virtue of learning a symmetric positive semi-definite (SPSD) stiffness matrix. We demonstrate, in a simplified setting, that learning an SPSD stiffness matrix results in a coarse-scale problem that is well-posed with a unique solution. We present numerical experiments on several exemplars, ranging from finite deformations of a cube to finite deformations with contact of a fastener-bushing geometry. We demonstrate that enforcing an SPSD stiffness matrix drastically improves the robustness and accuracy of FEM–ML coupled simulations, and that the resulting methods can accurately characterize out-of-sample loading configurations with significant speedups over the standard FEM simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Product-specific kinetics reveal effect of epoxy crystallization on thermoset thermal degradation

Crystallization is a common problem for epoxy resins, which are ubiquitous in industrial and commercial use. Integration of crystallized epoxy monomers into cured thermosets has been shown to alter the thermosets’ final mechanical properties. However, no studies have investigated the impact of these crystals on the thermal stability of the thermoset. Here we investigate the degradation kinetics of a bisphenol F–based epoxy thermoset with and without crystallized monomers using Product-Specific Kinetic (PSK) analysis coupled with Evolved Gas Analysis-Mass Spectrometry (EGA-MS). PSK analysis revealed significant differences in evolved product ion kinetics, suggesting changes in the degradation kinetics between thermoset configurations. Further, it was concluded that early stages of degradation are influenced most by crystal presence due to the high concentration of unreacted epoxy monomers and lower cross-linking density of the cured network. After post-cure annealing, significant changes are observed in the degradation kinetics of thermosets without crystal inclusions. Conversely, post-cure annealing procedures of crystal integrated thermosets showed little change in the thermoset degradation kinetics across all conversion extents. These findings suggest that post-cure annealing of thermosets with crystals present at the onset does not alter the cross-linking density of the polymer network enough to significantly change the degradation kinetics. We hypothesize this is because the excess monomers from the melted crystals are unable to find suitable reaction sites for complete binding into the polymer network, which has direct implications for the material properties and final thermal stability of the thermoset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Self-aligned heterogeneous quantum photonic integration

Integrated quantum photonics holds significant promise for scalable photonic quantum information processing, quantum repeaters, and quantum networks, but its development is hindered by the mismatch between materials hosting high-quality quantum emitters and those compatible with mature photonic technologies. Heterogeneous integration offers a potential solution to this challenge, yet practical implementations have been limited by inevitable insertion losses at material interfaces. Here, we present a self-aligned heterogeneous quantum photonic integration approach that enables near-unity coupling efficiency at the interface. To showcase our approach, we demonstrate Purcell enhancement of a silicon vacancy (SiV) center in diamond induced by a heterogeneous photonic crystal cavity defined by titanium dioxide (TiO 2 ), as well as optical spin control and readout via a TiO 2 photonic circuit. We further show that, when combined with inverse photonic design, our approach enables efficient and broadband collection of single photons from a color center into a heterogeneous waveguide. Our approach is not restricted to SiV centers or TiO 2 ; it has the potential to be broadly applied to integrate diverse solid-state quantum emitters with thin-film photonic devices where conformal deposition is possible. Together, these results establish a practical route to scalable quantum photonic integrated circuits that combine high-quality quantum emitters with technologically mature photonic platforms.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Topological controls on aluminosilicate glass dissolution: Complexities induced in hyperalkaline aqueous environments

Abstract Fly ash, an aluminosilicate composite consisting of disordered (major) and crystalline (minor) compounds, is a low‐carbon alternative that can partially replace ordinary portland cement (OPC) in the binder fraction of concrete. Therefore, understanding the reactivity of fly ash in the hyperalkaline conditions prevalent in concrete is critical to predicting concrete's performance; including setting and strength gain. Herein, temporal measurements of the solution composition (using inductively coupled plasma‐optical emission spectrometry: ICP‐OES) are used to assess the aqueous dissolution rate of monophasic synthetic aluminosilicate glasses analogous to those present in technical fly ashes, under hyperalkaline conditions (10 ≤ pH ≤ 13) across a range of temperatures (25°C ≤ T≤45°C). The dissolution rate is shown to depend on the average number of topological constraints per atom within the glass network (n c , unitless), but this dependence weakens with increasing pH (>10). This is postulated to be on account of: (a) time‐dependent changes in the glass’ surface structure, that is, the number of topological constraints; and/or (b) a change in the dissolution mechanism (eg from network hydrolysis to transport control). The results indicate that the topological description of glass dissolution is most rigorously valid only at very short reaction times (ie at high undersaturations), especially under conditions of hyperalkalinity. These findings provide an improved basis to understand the underlying factors that affect the initial and ongoing reactivity of aluminosilicate glasses such as fly ash in changing chemical environments, for example, when such materials are utilized in cementitious composites.

Oey, Tandré↗

DOE OSTI2022

Impact of Concurrent Solubilization and Fines Migration on Fracture Aperture Growth in Shales during Acidized Brine Injection

Acidic hydraulic fracturing fluid chemically and physically alters shale rock fabric during injection and shut-in, creating a “reaction-altered zone” along the fracture faces. To better characterize the variable thickness and composition of this reaction-altered zone under advective flow, we take a coupled experimental and modeling approach. A fluidic cell, with six fiducial markers, is first fabricated to keep the rock sample in place during the core floods and to allow image alignment of acquired images. Then, we conduct a series of reactive core floods in a clay-rich siliceous Wolfcamp shale sample with 10 wt % carbonate, using a synthetic fracturing fluid under no confining stress and at room temperature. High-resolution computed tomography (CT) scans are periodically conducted to observe the spatial alteration of the fracture network. We then perform scanning electron microscopy (SEM-EDS) on the two orthogonal surfaces (fracture surface and freshly cut profile face) to generate high-resolution elemental maps that show the change in mineralogy, both with distance along a given flow path along a fracture surface and with depth from the fracture surface into the shale matrix. These results are contrasted against a two-dimensional (2D) advection-diffusion reaction model developed previously for batch reactions between shale and synthetic fracturing fluids. The model simulates the geochemical interaction occurring at the fracture/matrix interface and penetrating into the shale matrix during the reactive core flood. Both model and experimental results show that the acidic brine is neutralized during the core flood, corresponding to an increase in fracture aperture as a function of fluid volume injected with the greatest change near the inlet. SEM-EDS scans reveal significant dissolution of carbonates on the fracture surface without pyrite oxidation. The reactive transport model indicates that carbonate depletion into the shale interior should be observable, yet SEM-EDS shows no discernible loss of carbonate in the orthogonal profile face. The combination of these observations suggests an additional fracture evolution mechanism in the reactive system, i.e., fines migration. We show that fines migration enhances the access of fracturing fluid to the matrix resulting in a more pronounced fracture widening. Finally, we conclude that coupled mineral dissolution and fines migration govern fracture aperture growth during acidized brine injection. In this work, we effectively show the underlying risk of relying solely on models that do not include an important (transport) process that can alter the system significantly and propose a combined chemomechanical mechanism for fracture evolution appropriate for this shale mineralogy.

58 GEOSCIENCES↗

DOE OSTI2018

An updated Lagrangian LBM–DEM–FEM coupling model for dual-permeability fissured porous media with embedded discontinuities

Many engineering applications and geological processes involve embedded discontinuities in porous media across multiple length scales (e.g. rock joints, grain boundaries, deformation bands and faults). Understanding the multiscale path-dependent hydro-mechanical responses of these interfaces across length scales is of ultimate importance for applications such as CO 2 sequestration, hydraulic fracture and earthquake rupture dynamics. While there exist mathematical frameworks such as extended finite element and assumed strain to replicate the kinematics of the interfaces, modeling the cyclic hydro-mechanical constitutive responses of the interfaces remains a difficult task. Here this paper presents a semi-data-driven multiscale approach that obtains both the traction-separation law and the aperture-porosity–permeability relation from micro-mechanical simulations performed on representative elementary volumes in the finite deformation range. To speed up multiscale simulations, the incremental constitutive updates of the mechanical responses are obtained from discrete element simulations at the representative elementary volume whereas the hydraulic responses are generated from a neural network trained with data from lattice Boltzmann simulations. These responses are then linked to a macroscopic dual-permeability model. This approach allows one to bypass the need of deriving multi-physical phenomenological laws for complex loading paths. More importantly, it enables the capturing of the evolving anisotropy of the permeabilities of the macro- and micro-pores. A set of numerical experiments are used to demonstrate the robustness of the proposed model.

42 ENGINEERING↗

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↗