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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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NASA NTRS2019

Characterizing the System Impulse Response Function from Photon-Counting LiDAR Data

NASA's Multiple Altimeter Beam Experimental LiDAR (MABEL) is an aircraft-based photon-counting laser altimeter designed as a simulator to test measurement techniques and algorithms for Advanced Topographic Laser Altimeter System (ATLAS), the sole instrument on NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission. By measuring the time of flight, pointing angle, and absolute position for individual photons, ICESat-2 provides detailed elevation measurements of earth's surface. Calculating accurate and precise elevations requires an understanding of how photons interact with surfaces, and characterization of the photon distribution after returning from surfaces. Neither MABEL nor ATLAS records the transmitted laser pulse shape, relying instead on aggregating several pulses worth of photons, often using histograms, to characterize the pulse shape. In this paper, we assess the limitations of using histograms and propose a more robust method to describe MABEL's system impulse-response function using an exponentially modified Gaussian distribution. We also provide standard error estimates for the arithmetic mean and standard deviation calculations, and for exponentially modified Gaussian parameters using a Monte Carlo sensitivity analysis. We apply this method to photon returns from a sea ice lead and from a dry salt lake bed as case studies for estimating the standard error associated with sample size for the arithmetic mean and standard deviation, and for the exponentially modified Gaussian parameters. We use these standard errors to calculate the minimum number of photons required to find both Gaussian and exponentially modified Gaussian distribution parameters within 3 cm of their parent population values.

photoncounting↗
NASA NTRS1962

An Experimental Investigation of the Performance of the Nitrogen Tetroxide-Hydrazine System in the Oxidizer-Rich and Fuel-Rich Regions

The results of an experimental program directed toward determining some of the operational characteristics of the nitrogen tetroxide-hydrazine propellant system under oxidizer-rich and fuel-rich conditions are reported. Data are presented for the mixture-ratio ranges of 0 to 0.55 at a nominal chamber pressure of 300 psia and characteristic chamber length of 250 in., and at mixture ratios of 5.9 to 13.0 at nominal chamber pressures of 400 and 500 psia and characteristic chamber lengths of 100 and 3900 in. The relationship of the actual performance data obtained in each region with those predicted from thermochemical performance calculations is presented. From this comparison, it is concluded that in neither region are equilibrium conditions obtained and that utilization of performance data obtained from assumptions of equilibrium will lead to serious errors. In the oxidizer-rich region investigated, temperatures considerably below those predicted occur throughout the range of mixture ratios investigated because of the lack of exothermic dissociation of the nitrogen oxides. In the fuel-rich region, below a mixture ratio of 0.3, temperatures considerably higher than those predicted occur because of the lack of endothermic dissociation of ammonia.

Chilenski, J. J.↗
NASA NTRS1999

The Role of Data Assimilation in the Study of Regional and Global Variability of the Hydrological Cycle

In the coming years, researchers will have at their disposal a host of new observations from advanced space-based sensors (e.g. the Tropical Rainfall Measuring Mission, EOS Terra and PM missions) providing, among other things, a more complete and accurate description of various components of the Earth's hydrological cycle. Also, increasingly more sophisticated and comprehensive geophysical models will provide researchers better tools for simulating the hydrological cycle, and for carrying out mechanistic studies of the role of moist processes in the climate system. In addition, new data sets generated with global four-dimensional data assimilation (4DDA) systems will provide comprehensive and complete information on both the state and forcing of the climate system. Ideally, the 4DDA systems optimally incorporate all relevant information from the observations together with a first guess from a state-of-the-art geophysical model to produce a "best" estimate of the climate state. Furthermore, to the extent that the assimilating models are realistic and are constrained by the observations, they should provide reliable estimates of the associated physical processes or climate forcing fields. While operational weather centers now have a considerable history of providing reliable estimates of the basic atmospheric state variables, the associated processes or diagnostic fields (which are less well constrained by the observations and sensitive to errors in the model's physical parameterizations) are still considered experimental and of uncertain quality. In this study we will examine the current generation of reanalysis products to assess the capabilities of 4DDA systems to represent components of the hydrological cycle. The focus is on the role of the model in providing consistent estimates of moist processes. We will also assess whether current observations provide sufficient constraints on these model- generated fields.

Schubert, Siegfried↗
NASA NTRS1998

Numerical Analysis of a Radiant Heat Flux Calibration System

A radiant heat flux gage calibration system exists in the Flight Loads Laboratory at NASA's Dryden Flight Research Center. This calibration system must be well understood if the heat flux gages calibrated in it are to provide useful data during radiant heating ground tests or flight tests of high speed aerospace vehicles. A part of the calibration system characterization process is to develop a numerical model of the flat plate heater element and heat flux gage, which will help identify errors due to convection, heater element erosion, and other factors. A 2-dimensional mathematical model of the gage-plate system has been developed to simulate the combined problem involving convection, radiation and mass loss by chemical reaction. A fourth order finite difference scheme is used to solve the steady state governing equations and determine the temperature distribution in the gage and plate, incident heat flux on the gage face, and flat plate erosion. Initial gage heat flux predictions from the model are found to be within 17% of experimental results.

Jiang, Shanjuan↗
DOE OSTI2024

In-situ strain behavior and BISON simulations of Zircaloy cladding subjected to temperature cycling separate-effects tests in a steam environment

Understanding fuel system performance during anticipated transients without scram (ATWSs) in boiling water reactors (BWRs) is necessary for refining current and future safety limits. High-fidelity material models and simulations are fundamental to rigorous assessment of zirconium-based cladding performance. However, experimental thermomechanical data during simulated ATWSs to validate these modes are limited. To provide relevant in-situ data, Zircaloy-4 cladding was subjected to cyclic heating in a steam environment to simulate an out-of-pile BWR ATWS. Digital image correlation was used to capture the cladding strain behavior in-situ for comparison against simulations using the BISON finite element code. Conventional high-temperature models were compared using multiple schemes to gain a better understanding of the applicability of three BISON models to BWR ATWS: (1) the default combination of creep models in BISON, (2) the high-temperature Erbacher model alone, and (3) the low-temperature Limback-Andersson model alone. The cases run with the Limback-Andersson model alone produced the lowest root mean square error (RMSE). The lowest RMSE for the Limback-Andersson model alone was 0.659%, and the highest RMSE reported was 4.22%. A data gap within the model in the temperature regime of interest was also identified, and to account for this gap, the current model in BISON is linearly interpolated between two separate datasets. In conclusion, this evaluation highlights the need to either develop a new model or to improve the existing model to capture transient creep effects resulting from a cyclic temperature transient.

Anticipated transient without SCRAM↗
DOE OSTI2025

FedEFsz: Fair Cross-Silo Federated Learning System With Error-Bounded Lossy Compression

Cross-Silo federated learning systems have been identified as an efficient approach to scaling DNN training across geographically-distributed data silos to preserve the privacy of the training data. Communication efficiency and fairness are two major issues that need to be both satisfied when federated learning systems are deployed in practice. Simultaneously guaranteeing both of them, however, is exceptionally difficult because simply combining communication reduction and fairness optimization approaches often causes non-converged training or drastic accuracy degradation. Here, to bridge this gap, we propose FedEFsz. On the one hand, it integrates the state-of-the-art error-bounded lossy compressor SZ3 into cross-silo federated learning systems to significantly reduce communication traffic during the training. On the other hand, it achieves a high fairness (i.e., rather consistent model accuracy and performance across different clients) through a carefully designed heuristic algorithm that can tune the error-bound of SZ3 for different clients during the training. Extensive experimental results based on a GPU cluster with 65 GPU cards show that FedEFsz improves the fairness across different benchmarks by up to 60.88% and meanwhile reduces the communication traffic by up to 315×.

Cross-Silo Federated Learning Systems↗
DOE OSTI2025

Attention to quantum complexity

The imminent era of error-corrected quantum computing demands robust methods to characterize quantum state complexity from limited, noisy measurements. We introduce the Quantum Attention Network (QuAN), a classical artificial intelligence (AI) framework leveraging attention mechanisms tailored for learning quantum complexity. Inspired by large language models, QuAN treats measurement snapshots as tokens while respecting permutation invariance. Combined with our parameter-efficient miniset self-attention block, this enables QuAN to access high-order moments of bit-string distributions and preferentially attend to less noisy snapshots. We test QuAN across three quantum simulation settings: driven hard-core Bose-Hubbard model, random quantum circuits, and toric code under coherent and incoherent noise. QuAN directly learns entanglement and state complexity growth from experimental computational basis measurements, including complexity growth in random circuits from noisy data. In regimes inaccessible to existing theory, QuAN unveils the complete phase diagram for noisy toric code data as a function of both noise types, highlighting AI’s transformative potential for assisting quantum hardware.

Kim, Hyejin [Cornell Univ., Ithaca, NY (United Sta↗
DOE OSTI2022

Oxygen diffusion in garnet: Experimental calibration and implications for timescales of metamorphic processes and retention of primary O isotopic signatures

Knowledge of oxygen diffusion in garnet is crucial for a correct interpretation of oxygen isotope signatures in natural samples. A series of experiments was undertaken to determine the diffusivity of oxygen in garnet, which remains poorly constrained. The first suite included high-pressure (HP), nominally dry experiments performed in piston-cylinder apparatus at: (1) T = 1050–1600 °C and P = 1.5 GPa and (2) T = 1500 °C and P = 2.5 GPa using yttrium aluminum garnet (YAG; Y3Al5O12) cubes. Second, HP H2O-saturated experiments were conducted at T = 900 °C and P = 1.0–1.5 GPa, wherein YAG crystals were packed into a YAG + Corundum powder, along with 18O-enriched H2O. Third, 1 atm experiments with YAG cubes were performed in a gas-mixing furnace at T = 1500–1600 °C under Ar flux. Finally, an experiment at T = 900 °C and P = 1.0 GPa was done using a pyrope cube embedded into pyrope powder and 18O-enriched H2O. Experiments using grossular were not successful. Profiles of 18O/(18O+16O) in the experimental charges were analyzed with three different secondary ion mass spectrometers (SIMS): sensitive high-resolution ion microprobe (SHRIMP II and SI), CAMECA IMS-1280, and NanoSIMS. Considering only the measured length of 18O diffusion profiles, similar results were obtained for YAG and pyrope annealed at 900 °C, suggesting limited effects of chemical composition on oxygen diffusivity. However, in both garnet types, several profiles deviate from the error function geometry, suggesting that the behavior of O in garnet cannot be fully described as simple concentration-independent diffusion, certainly in YAG and likely in natural pyrope as well. The experimental results are better described by invoking O diffusion via two distinct pathways with an inter-site reaction allowing O to move between these pathways. Modeling this process yields two diffusion coefficients (D values) for O, one of which is approximately two orders of magnitude higher than the other. Taken together, Arrhenius relationships are:logDm2s-1=-7.2(±1.3)+(-321(±32)kJmol-12.303RT) for the slow pathway, andlogDm2s-1=-5.4(±0.7)+(-321(±20)kJmol-12.303RT) for the fast pathway. We interpret the two pathways as representing diffusion following vacancy and inter-stitial mechanisms, respectively. Regardless, our new data suggest that the slow mechanism is prevalent in garnet with natural compositions, and thus is likely to control the retentivity of oxygen isotopic signatures in natural samples. The diffusivity of oxygen is similar to Fe-Mn diffusivity in garnet at 1000–1100 °C and Ca diffusivity at 850 °C. However, the activation energy for O diffusion is larger, leading to lower diffusivities at P-T conditions characterizing crustal metamorphism. Therefore, original O isotopic signatures can be retained in garnets showing major element zoning partially re-equilibrated by diffusion, with the uncertainty caveat of extrapolating the experimental data to lower temperature conditions.

Geochemistry & Geophysics↗
NASA NTRSDate not supplied

Preliminary Steps in Developing Rapid Aero Modeling Technology

The Rapid Aero Modeling (RAM) approach is a method to efficiently and automatically obtain aerodynamic models during testing, significantly saving time and resources. Motivation for this technology results from demand for experimental efficiency and model fidelity that has increased with growing aircraft complexity and aerodynamic nonlinearities. These issues are typical in the responses presented by a class of vehicles categorized as Urban Air Mobility aircraft where many features from both airplane and rotorcraft are present. For UAM configurations, with typically many more factors than conventional aircraft, traditional test methods can lead to increased costs and missed interactions. RAM guides the test to obtain high-fidelity, statistically rigorous aircraft models, and the approach is applicable to computational, ground, or flight-test experiments. It combines concepts from Design of Experiment theory and Aircraft System Identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity typically, in terms of prediction error. RAM only collects data required to meet the user-specified fidelity and fidelity is only limited by the facility and test article capabilities. An initial wind tunnel test to support development of RAM was conducted to assess potential metrics, algorithms, and procedures. This paper presents results from initial tests for the development of RAM technology and highlights some of the unique features of RAM applied toa conventional configuration during a ground-based, static, wind-tunnel test.

Patrick C Murphy↗
NASA NTRS1972

An evaluation of random analysis methods for the determination of panel damping

An analysis is made of steady-state and non-steady-state methods for the measurement of panel damping. Particular emphasis is placed on the use of random process techniques in conjunction with digital data reduction methods. The steady-state methods considered use the response power spectral density, response autocorrelation, excitation-response crosspower spectral density, or single-sided Fourier transform (SSFT) of the response autocorrelation function. Non-steady-state methods are associated mainly with the use of rapid frequency sweep excitation. Problems associated with the practical application of each method are evaluated with specific reference to the case of a panel exposed to a turbulent airflow, and two methods, the power spectral density and the single-sided Fourier transform methods, are selected as being the most suitable. These two methods are demonstrated experimentally, and it is shown that the power spectral density method is satisfactory under most conditions, provided that appropriate corrections are applied to account for filter bandwidth and background noise errors. Thus, the response power spectral density method is recommended for the measurement of the damping of panels exposed to a moving airflow.

Bhat, W. V.↗
DOE OSTI2023

Quantifying uncertainty in analysis of shockless dynamic compression experiments on platinum. II. Bayesian model calibration

Dynamic shockless compression experiments provide the ability to explore material behavior at extreme pressures but relatively low temperatures. Typically, the data from these types of experiments are interpreted through an analytic method called Lagrangian analysis. Here, in this work, alternative analysis methods are explored using modern statistical methods. Specifically, Bayesian model calibration is applied to a new set of platinum data shocklessly compressed to 570 GPa. Several platinum equation-of-state models are evaluated, including traditional parametric forms as well as a novel non-parametric model concept. The results are compared to those in Paper I obtained by inverse Lagrangian analysis. The comparisons suggest that Bayesian calibration is not only a viable framework for precise quantification of the compression path, but also reveals insights pertaining to trade-offs surrounding model form selection, sensitivities of the relevant experimental uncertainties, and assumptions and limitations within Lagrangian analysis. The non-parametric model method, in particular, is found to give precise unbiased results and is expected to be useful over a wide range of applications. The calibration results in estimates of the platinum principal isentrope over the full range of experimental pressures to a standard error of 1.6%, which extends the results from Paper I while maintaining the high precision required for the platinum pressure standard.

Brown, Justin Lee↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Classification Analytics of Pu-239 and U-235 Source Signatures Using Gamma Spectral Regions

Machine learning detection methods using gamma signatures from spectral measurements of low-intensity Pu-239 and U-235 sources are studied. NaI detectors located at different distances fromthe source have been used to collect the training and independent testing data sets. The source is introduced via a shielded conduit into the facility where it is surrounded by 21 NaI detectors deployed over 6 x 6 meters area in the formation of two concentric circles and a spiral. The counts in gamma spectral regions associated with these two sources are estimated at 1 second intervals for each NaI detector, and are used as classifier features for detecting the source presence. Eight different classifiers with five basic properties — namely, smooth, non-smooth, statistical, structural, and hyper-parameter tuning — are trained and tested using the background and source measurements collected over multiple experimental runs. While the overall classifier performance improved as detectors closer to the source are used, some identically produced detectors under-performed but differently between two sources. Some classifiers achieved lower training error but their testing error based on independent measurements is higher for both sources. Overall, these results indicate significant over-fitting by these methods, and illustrate the complexity of training and selecting the machine learning methods to solve these detection problems.

Rao, Nageswara↗
DOE OSTI2023

Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph transformer

Abstract Motivation The state-of-art protein structure prediction methods such as AlphaFold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network to directly refine protein atomic coordinates in a predicted tertiary structure represented as a molecular graph. Results The method is first trained and tested on the structural models in AlphaFoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets and 7 CASP14 refinement targets. ATOMRefine improves the quality of both backbone atoms and all-atom conformation of the initial structural models generated by AlphaFold. It also performs better than two state-of-the-art refinement methods in multiple evaluation metrics including an all-atom model quality score—the MolProbity score based on the analysis of all-atom contacts, bond length, atom clashes, torsion angles, and side-chain rotamers. As ATOMRefine can refine a protein structure quickly, it provides a viable, fast solution for improving protein geometry and fixing structural errors of predicted structures through direct coordinate refinement. Availability and implementation The source code of ATOMRefine is available in the GitHub repository (https://github.com/BioinfoMachineLearning/ATOMRefine). All the required data for training and testing are available at https://doi.org/10.5281/zenodo.6944368.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Experimental measurement of structural power flow on an aircraft fuselage

An experimental technique is used to measure structural intensity through an aircraft fuselage with an excitation load applied near one of the wing attachment locations. The fuselage is a relatively large structure, requiring a large number of measurement locations to analyze the whole of the structure. For the measurement of structural intensity, multiple point measurements are necessary at every location of interest. A tradeoff is therefore required between the number of measurement transducers, the mounting of these transducers, and the accuracy of the measurements. Using four transducers mounted on a bakelite platform, structural intensity vectors are measured at locations distributed throughout the fuselage. To minimize the errors associated with using the four transducer technique, the measurement locations are selected to be away from bulkheads and stiffeners. Furthermore, to eliminate phase errors between the four transducer measurements, two sets of data are collected for each position, with the orientation of the platform with the four transducers rotated by 180 degrees and an average taken between the two sets of data. The results of these measurements together with a discussion of the suitability of the approach for measuring structural intensity on a real structure are presented.

Cuschieri, J. M.↗
NASA NTRS1995

Rotational Energy Transfer and Collisional Induced Raman Linewidths in N2 Gas: Energy Transfer Rates - 1

Rotationally inelastic transitions of N2 have been studied in the coupled state (CS) and infinite-order-sudden (IOS) approximations, using the N2-N2 rigidrotor potential of van der Avoird et al. For benchmarking purposes, close coupling (CC) calculations have also been carried out over a limited energy range. The CC and CS cross sections have been obtained both with and without identical molecule exchange symmetry, whereas exchange was neglected in the IOS calculations. The CS results track the CC cross sections rather well; between 113 - 219 cm(exp -1) the average deviation is 14%. Comparison between the CS and IOS cross sections at the high energy end of the CS calculation, 500 - 680 cm(exp -1), shows that IOS is sensitive to the amount of inelasticity and the results for large DELTA J transitions are subject to larger errors. It is found that the state-to-state cross sections with even and odd exchange symmetry agree to better than 2% and are well represented as a sum of direct and exchange cross sections for distinguishable molecules, an indication of the applicability of a classical treatment for this system. This result, however, does not apply to partial cross sections for given total J, but arises from a near cancellation in summing over partial waves. In order to use rigid-rotor results for the calculation of effective rotational excitation rates of N2 in the v=1 vibrational level colliding with bath N2 molecules in the v=0 level, it is assumed that exchange scattering between molecules in different vibrational levels is negligible and direct scattering is independent of Y. Good agreement with room temperature experimental data is obtained. The effective rates determined using the IOS and energy corrected sudden (ECS) approximations are also in reasonable agreement with experiment, with the ECS results being somewhat better. The problem with a degeneracy factor in earlier cross section expressions for collisions between identical molecules is pointed out and corrected.

Huo, Winifred M.↗
DOE OSTI2025

Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry‐Informed Transfer Learning

Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich–Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi‐empirical relationships (RK), data from the thermophysical arm of the molten salt thermal properties database and universal ab initio properties of component mixtures taken from the joint automated repository for various integrated simulations (JARVIS) classical force‐field inspired descriptors database to predict density in molten salts. Herein, it is shown that DNNs predict molten salt density with an r 2 over 0.99 and a mean absolute percentage error under 1%, outperforming alternative methods.

inorganic materials↗
DOE OSTI2024

Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗