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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Molecular simulation using transfer-learned potentials for the disordered nanoscale structure of nitrogen-doped nanoporous carbons

Machine learning (ML)-based molecular dynamics (MD) simulations of the formation of a class of N-doped nanoporous carbons are performed to assess their disordered partially graphitized nanoscale structure. The study is motivated by the effectiveness of so-called nitrogen assembly carbons (NACs) for catalysis applications. Benchmark simulations for pure-C disordered graphitic systems reveal the importance of reliably capturing the vdW component of the potentials in order to accurately describe the tendency for layering of disordered graphene-like sheets. In our modeling, this is achieved by a transfer learning strategy incorporating features of the energetics from the optB88-vdW DFT functional into potentials initially trained with a less expensive functional, thereby providing a superior description of the pure-C systems. Generation from MD simulations of realistic partially graphitized structures is significantly more challenging for N-doped versus for pure C systems. However, such structures are achieved by a tailored MD simulation protocol mimicking the experimental synthesis process and in particular incorporating an annealing and subsequent quenching stages. Simulated PXRD patterns effectively reproduce the features of experimental observations for NACs, including the appearance of a prominent but broad (002) peak at around 25, and the development of another weaker feature associated with in-layer ordering of mixed C-N graphene-like sheets.

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

Probing autoionization decay lifetimes of the 4d−16ℓ core-excited states in xenon using attosecond noncollinear four-wave-mixing spectroscopy

The decay of core-excited states is a sensitive probe of autoionization dynamics and correlation effects in many-electron systems, occurring on ultrafast timescales. Xenon, with its dense manifold of autoionizing resonances that can be coupled with near-infrared light, provides a platform to investigate these processes. In this work, the autoionization decay lifetimes of 4d-16ℓ (ℓ = s, p, d, …) core-excited states in xenon atoms are probed with extreme ultraviolet (XUV) attosecond noncollinear four-wave-mixing (FWM) spectroscopy. The 4d{5/2,3/2}-16p XUV-bright states (optically dipole-allowed) exhibit decay lifetimes of ∼6 fs, which is consistent with spectator-type decay. In contrast, the 4d{5/2,3/2}-16s and 4d{5/2,3/2}-16d XUV-dark states (optically dipole forbidden) show longer decay lifetimes of ∼20 fs. Photoionization calculations confirm that all core-hole states with 4d character should decay via spectator channels in ≤6 fs, suggesting that the apparently longer dark-state decay times arise from an alternative mechanism. A few-level simulation of the FWM process shows that the inclusion of a nearby, longer-lived dark state can mimic the experimental FWM signal, suggesting population cycling with a second electronic state with non-4d character. Ab initio calculations support the presence of such multielectron excited states in the 60-70 eV range. These results demonstrate that FWM signals can encode coupled-state dynamics when probing complex systems, highlighting the importance of combining theoretical and experimental approaches to disentangle accurate core-level decay pathways and lifetimes.

Puskar, Nicolette G↗
DOE OSTI2025

Characterizing leaf-scale fluorescence with spectral invariants

Sun-induced chlorophyll fluorescence (SIF) is increasingly recognized as a non-destructive probe for tracking terrestrial photosynthesis. Emerging developments in spectral invariants theory provide an innovative and efficient approach for representing SIF radiative transfer processes at the canopy scale. However, modeling leaf-scale fluorescence based on the spectral invariants properties (SIP) remains underexplored. In this study, the spectral invariants theory is employed for the first time to model the leaf-scale total, backward and forward fluorescence (leaf-SIP SIF). The leaf-SIP SIF model separates the leaf-scale radiative transfer process into two distinct components: the wavelength-dependent one associated with leaf biochemical properties, and the wavelength-independent component linked to leaf structural characteristics. The leaf structure-related effects are characterized by two spectrally invariant parameters: the photon recollision probability (p) and the scattering asymmetry parameter (q), which are parameterized using the directly measurable leaf dry matter. Evaluation against field measurements shows that the proposed leaf-SIP SIF model has a good performance, with coefficient of determination (R 2 ) of 0.89, 0.89, 0.90 and root mean squared errors (RMSE) of 1.28, 0.69, 0.74 Wm -2 µm -1 sr -1 , respectively for the total, backward, and forward fluorescence (660–800 nm). The leaf-SIP SIF model with a more concise formulation demonstrates comparable performance with the widely used Fluspect model. Further, the leaf-SIP SIF model provides a simple and efficient approach for simulating leaf-scale fluorescence, with the potential to be integrated into a unified SIP-based model framework for simulating the radiative transfer processes across the soil-leaf-canopy-atmosphere continuum.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Simulation of Creep Deformation and Failure in Graded AM Microstructures

This report describes modeling tools and techniques developed to simulate the long-term material performance of 316H stainless steel manufactured using Laser Powder Bed Fusion (LPBF). A physics-based Crystal Plasticity Finite Element model is used to simulate creep in microstructures and to study the roles of grain morphology, porosity, and texture. We describe our modeling methodology, including an orientation-mapping technique to capture the spatially varying crystallographic orientation that results from the build conditions. Our study of microstructural features shows that AM microstructures produced by LPBF tend to creep faster in the build direction, while texture and grain boundaries strengthen the transverse directions. However, when grain-boundary porosity and the consequent cavity growth are included in the model, the transverse directions begin to creep faster. In examining texture, the results indicate that spatially varying orientation arising from the build conditions increases anisotropy in the material, making it critical to account for orientation gradients in the material to accurately model its mechanical behavior. We also describe a material-model calibration campaign in which we calibrated the constitutive model specifically for LPBF 316H stainless steel at 725℃ for both solution-annealed and as-built conditions. Finally, these tools and techniques are used to model creep in microstructures representing different regions of an LPBF material with graded microstructure, owing to intentional variation in processing conditions. The creep simulation results show good agreement with experimental data across all three microstructures, with future work planned to study rupture in the material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Information Length Concept Applied to Plasma Turbulence

A methodology to study statistical properties of anomalous transport in fusion plasma is investigated. Three time traces generated by the full-f gyrokinetic code GKNET are analyzed for this purpose. The time traces consist of heat flux as a function of the radial position, which is studied in a novel manner using statistical methods. The simulation data exhibit transport processes with both medium and long correlation length along the radius. A typical example of a phenomenon with long correlation length is avalanches. In order to investigate the evolution of the turbulent state, two basic configurations are studied, one flux-driven and one gradient-driven with decaying turbulence. The information length concept in tandem with Boltzmann–Gibbs and Tsallis entropy is used in the investigation. It is found that the dynamical states in both flux-driven and gradient-driven cases are surprisingly similar, but the Tsallis entropy reveals differences between them. This indicates that the types of probability distribution function are nevertheless quite different since the higher moments are significantly different.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Enhancing EV Motor Design Through Knowledge-Based AI and Hierarchical Fuzzy Logic Model

This work presents a novel approach to optimizing electric vehicle motor design through the integration of Knowledge-Based Artificial Intelligence (KB-AI) and Hierarchical Fuzzy Logic. Traditional motor design processes are time-intensive, relying heavily on iterative simulations and domain-specific expertise. These processes are further complicated by the nonlinear relationships between key design parameters. The proposed framework addresses these challenges by systematically encoding expert knowledge from scientific literature into a fuzzy logic system, allowing for the efficient handling of complex design variables. The hierarchical fuzzy logic model reduces computational complexity by decomposing the nonlinear relationships into manageable rule sets while maintaining design accuracy. The proposed methodology was applied to the design of a 100 kW motor, yielding optimal values for key parameters. This resulted in a compact motor design with a volume of 2.2 liters, showcasing the framework’s ability to deliver high-performance, application-specific motor configurations.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
DOE OSTI2025

Acceleration of the particle-in-cell code Osiris with graphics processing units

Fully relativistic particle-in-cell (PIC) simulations are crucial for advancing our knowledge of plasma physics. Modern supercomputers based on graphics processing units (GPUs) offer the potential to perform PIC simulations of unprecedented scale, but require robust and feature-rich codes that can fully leverage their computational resources. In this work, this demand is addressed by adding GPU acceleration to the PIC code Osiris. An overview of the algorithm, which features a CUDA extension to the underlying Fortran architecture, is given. Detailed performance benchmarks for thermal plasmas are presented, which demonstrate excellent weak scaling on NERSC's Perlmutter supercomputer and high levels of absolute performance. The robustness of the code to model a variety of physical systems is demonstrated via simulations of Weibel filamentation and laser-wakefield acceleration run with dynamic load balancing. Finally, measurements and analysis of energy consumption are provided that indicate that the GPU algorithm is up to ~14 times faster and ~7 times more energy efficient than the optimized CPU algorithm on a node-to-node basis. The described development addresses the PIC simulation community's computational demands both by contributing a robust and performant GPU-accelerated PIC code and by providing insight into efficient use of GPU hardware.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Technoeconomic analysis and life cycle assessment of purification processes for captured CO 2 streams

Captured carbon dioxide (CO 2 ) streams contain impurities that must be removed to meet specifications for safe transport, storage, and utilization. Among these impurities, oxygen poses challenges due to its high reactivity and potential to cause corrosion, motivating stringent purity limits below 10 ppmv. Building on recent experimental demonstrations of catalytic oxygen removal using hydrogen (H 2 ), carbon monoxide (CO), methanol (CH 3 OH), and methane (CH 4 ) as reducing agents, this study presents a technoeconomic (TEA) and life cycle assessment (LCA) of these four catalytic purification pathways. Process flowsheets were developed and simulated in Aspen Plus for CO 2 streams representative of both low-temperature and high-temperature capture processes, with integrated heat recovery and energy optimization. Results showed that total purification costs were dominated by feedstock procurement and electricity consumption. Among the studied reducing agents, the CH 4 -assisted route achieved the lowest purification cost and highest CO 2 recovery. Sensitivity analyses showed that the H 2 route became competitive at H 2 prices below $\$$0.56/kg to $\$$0.84/kg, depending on the CO 2 feed temperature conditions. In conclusion, environmental impacts were primarily driven by indirect CO 2 emissions from raw material production and utility consumption.

CO2 pipeline specifications↗
DOE OSTI2025

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗
DOE OSTI2025

Processing behavior evolution of recycled polypropylene: An integrated experimental and Computer-Aided engineering simulation study

Polypropylene (PP) comprises 21% of global plastics production and 18% of plastics waste, yet less than 1% of solid-waste PP is recycled in the United States (U.S.), representing significant environmental and economic challenges. Mechanical recycling, the most prevalent recycling method, subject's materials to thermomechanical stresses, which typically degrade polymer properties, affecting the quality of polymer products. This study replicates the impact of mechanical recycling through multiple extrusion cycles to examine the effects on PP's processing behavior. Dynamic scanning calorimetry (DSC) measurements showed stable melting behavior across all processing conditions, while crystallization analysis exhibited consistent shifts in kinetic parameters. Rheological characterization demonstrated progressive viscosity reductions through successive cycles, particularly pronounced at elevated reprocessing temperatures. Here, the integration of this experimental data into injection molding simulations showed that recycled PP maintains viable processing characteristics. Our findings establish quantitative correlations between processing history and material behavior, enabling optimization of processing parameters directly rather than relying on trial-and-error approaches. While these results reflect idealized recycling conditions with minimal contamination, they provide a framework for understanding fundamental property evolution during mechanical recycling.

42 ENGINEERING↗
DOE OSTI2026

From Pyrolysis Oil to Advanced Biographite Anode: Unravelling Biocoke Structural Evolution and Delayed Coking Effects

Catalytic graphitization of biomass-derived carbon offers a promising route to produce biographite as a sustainable alternative to petroleum-based synthetic graphite for lithium-ion battery (LIB) anodes. This study investigates the physicochemical properties of biocokes produced from pyrolysis oil at carbonization temperatures ranging from 150 degrees C to 500 degrees C. Using an iron (Fe) catalyst, graphitization was performed at 1500 degrees C, significantly lower than the ~3000 degrees C required for conventional synthetic graphite. The effects of introducing an intermediate-temperature hold (400 degrees C-600 degrees C) prior to graphitization were evaluated, simulating a "delayed coking" process to enable the coproduction of sustainable aviation fuels (SAFs). Chemical structure evolution during biocoke formation was analyzed, and proposed mechanisms are presented. Biographites produced via the delayed coking pathway exhibited high crystallinity and excellent electrochemical performance in both half-cell and full-cell LIB configurations. The full cells exhibited an initial discharge capacity close to the theoretical capacity of the NMC622 cathode (175 mAh/g at 4.2 V), and high capacity retention (~88%) after 150 cycles. Notably, the graphitic and electrochemical properties remained stable across the range of intermediate hold temperatures. These findings provide a foundation for optimizing temperature parameters in delayed coking systems to enable scalable, integrated production of biographite and SAFs from pyrolysis oil.

09 BIOMASS FUELS↗
DOE OSTI2024

Improving the reliability of machine learned potentials for modeling inhomogeneous liquids

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids—including the density response, surface tension and size-dependent cavitation free energies—for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

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

Semi-supervised permutation invariant particle-level anomaly detection

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Earth-catalyzed detection of magnetic inelastic dark matter with photons in large underground detectors

Inelastic dark matter with moderate splittings, $\mathcal{O}$ (few to 150) keV, can upscatter to an excited state in the Earth, with the excited state subsequently decaying, leaving a distinctive monoenergetic photon signal in large underground detectors. The photon signal can exhibit sidereal-daily modulation, providing excellent separation from backgrounds. Using a detailed numerical simulation, we examine this process as a search strategy for magnetic inelastic dark matter with the dark matter mass near the weak scale, where the upscatter to the excited state and decay proceed through the same magnetic dipole transition operator. At lower inelastic splittings, the scattering is dominated by moderate mass elements in the Earth with high spin, especially 27 Al, while at larger splittings, 56 Fe becomes the dominant target. We show that the proposed large volume gaseous detector CYGNUS will have excellent sensitivity to this signal. Xenon detectors also provide excellent sensitivity through the inelastic nuclear recoil signal, and if a future signal is seen, we show that the synergy among both types of detection can provide strong evidence for magnetic inelastic dark matter. In the course we have calculated nuclear response functions for elements relevant for scattering in the Earth, which are publicly available on GitHub.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Best of both worlds: Enforcing detailed balance in machine learning models of transition rates

The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the a priori tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. While conventional KMC methods based on transition state theory naturally yield reversible dynamics that exactly obey the detailed balance criterion, providing strong guarantees on the properties of the stationary distribution, many recently-proposed ML-based approaches to barrier predictions provide no such guarantees. In this study, we derive conditions under which physics-informed ML architectures exactly enforce the detailed balance condition by construction, even when relying on non-extensive descriptions of states in terms of local environments around mobile defects. In conclusion, using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost.

36 MATERIALS SCIENCE↗