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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Decoding structure-spectrum relationships with physically organized latent spaces

Here, a semisupervised machine learning method for the discovery of structure-spectrum relationships is developed and then demonstrated using the specific example of interpreting x-ray absorption near-edge structure (XANES) spectra. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. Specifically, an adversarial autoencoder is augmented with a rank constraint (RankAAE). The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor. As a part of this process, the model provides a robust and quantitative measure of the structure-spectrum relationship by decoupling intertwined spectral contributions from multiple structural characteristics. This makes it ideal for spectral interpretation and the discovery of descriptors. The capability of this procedure is showcased by considering five local structure descriptors and a database of >50 000 simulated XANES spectra across eight first-row transition metal oxide families. The resulting structure-spectrum relationships not only reproduce known trends in the literature but also reveal unintuitive ones that are visually indiscernible in large datasets. The results suggest that the RankAAE methodology has great potential to assist researchers in interpreting complex scientific data, testing physical hypotheses, and revealing patterns that extend scientific insight.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Unifying principles for catalytic hydrotreating processes (Final Technical Report)

This project builds on the hypothesis that the hydrotreating processes for the removal of oxygen and sulfur are fundamentally similar at the atomic-scale and existing knowledge from the treatment of petroleum derived feedstock can be leveraged for the design of novel catalysts for the upgrade of bio-oil. We tested this hypothesis by comparing computed potential energy diagrams for hydrodesulfurization (HDS) of thiophene over MoS 2 with hydrodeoxygenation (HDO) of furan over MoO 3 and concluded that certain aspects, such as catalyst promotion with transition metals, are valid strategies for both reactions. On the other hand, we also noticed significant differences in the mechanism for hydrogen (H 2 ) activation, which requires sites with metallic character. While MoS 2 is known to have metallic edge states that can catalyze H 2 dissociation, this elementary step is prohibitively slow on defect-free oxides. Only in the presence of vacancies or by creating metal/oxide interfaces can efficient H 2 activation sites during HDO be formed. The need for bifunctional catalyst when it comes to efficient and selective HDO or dehydrogenation reactions was further corroborated in joint experimental and theoretical studies of the Guerbet reaction for the coupling of biomass derived oxygenates over PdCu alloys, nitrate reduction over In-promoted Pd nanoparticles, and ethylene dehydroaromatization over Ga-exchanged ZSM-5 zeolites. All of these catalytic systems have in common that catalytic sites with distinct functional requirements are needed to create a working catalyst. Detailed computational studies were carried out for HDO of m -cresol and phenol on Ru-modified TiO 2 surfaces, which allowed us to attribute catalytic activity to the metal/oxide interface. A surprising finding was that heterolytic cleavage of the H-H bond across the Ru/TiO 2 interface was critically important, despite lower barriers for homolytic H 2 activation on Ru metal. The explanation lies in the high barriers for hydrogen spillover from Ru to TiO 2 , which becomes unnecessary in the heterolytic activation pathway. Moreover, we also reported that proton transfer steps between metal and oxide sites are mediated by weakly adsorbed surface water. During attempts to develop and validate a kinetic Monte Carlo (kMC) model for HDO reactions at the Ru/TiO 2 interface, it became clear that lateral interactions are paramount to describe realistic surface chemistry and without these interactions, the reduction and hydroxylation behavior from our simulations was inconsistent with reported experiments. To assess the importance of lateral interactions in popular computational catalyst design strategies relying on the identification of reactivity descriptors, which can be used along with Brønsted–Evans–Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends, we compared predicted trends with those obtained from descriptor-based kMC models. We critically evaluated the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. The mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, we concluded that the MKM approach does provide an overall better entry point for computational catalyst design. Overall, this project has led to 11 peer-reviewed publications, and their scientific is impact is well illustrated by their combined 702 citations.

08 HYDROGEN↗
DOE OSTI2025

Advancing Multiscale Simulation of Plasma-Surface Interfaces

We report the development of an atomistic-informed, surface-state-dependent predictive model for particle exchange in a carbon-tungsten plasma-surface interface. The predictive model uses machine learning (ML) techniques to learn the energy and angular distributions for particle exchange and rate functions for surface state evolution from molecular dynamics simulations of cumulative bombardment of tungsten by energetic carbon ions. Each predictive component is sensitive to the energy and trajectory of incident plasma species and the surface state. The surface state is represented by a set of surface state descriptors, which were derived from the atomistic surface state for each independent carbon bombardment event. These descriptors are representative of the composition and degree of amorphization of the outermost angstrom of surface material and were chosen to optimize predictive performance for particle exchange at the interface. The distributions for particle exchange (reflection/sputtering) are demonstrated to vary with each surface state descriptor, motivating the development of surface-state-dependent particle exchange models for plasma simulations. The performance of various ML methods was compared, including polynomial quantile regression, artificial neural networks, k-nearest neighbors, and random forest algorithms, with polynomial regression performing the best for interpolation and extrapolation of learned relationships. In addition to the particle exchange model, a neutral network was developed and used to identify data sufficiency throughout surface descriptor space, which will enable real-time feedback during future data production to ensure data is produced where it is most needed, and we provide commentary on improvements to the data production workflow for future endeavors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

A laboratory study of subjective annoyance response to sonic booms and aircraft flyovers

Three experiments were conducted to determine subjective equivalence of aircraft subsonic flyover noise and sonic booms. Two of the experiments were conducted in a loudspeaker-driven sonic boom simulator, and the third in a large room containing conventional loudspeakers. The sound generation system of the boom simulator had a frequency response extending to very low frequencies (about 1 Hz) whereas the large room loudspeakers were limited to about 20 Hz. Subjective equivalence between booms and flyovers was quantified in terms of the difference between the noise level of a boom and that of a flyover when the two were judged equally annoying. Noise levels were quantified in terms of the following noise descriptors: Perceived Level (PL), Perceived Noise Level (PNL), C-weighted sound exposure level (SELC), and A-weighted sound exposure level (SELA). Results from the present study were compared, where possible, to similar results obtained in other studies. Results showed that noise level differences depended upon the descriptor used, specific boom and aircraft noise events being compared and, except for the PNL descriptor, varied between the simulator and large room. Comparison of noise level differences obtained in the present study with those of other studies indicated good agreement across studies only for the PNL and SELA descriptors. Comparison of the present results with assessments of community response to high-energy impulsive sounds made by Working Group 84 of the National Research Council's Committee on Hearing, Bioacoustics, and Biomechanics (CHABA) showed good agreement when boom/flyover noise level differences were based on SELA. However, noise level differences obtained by CHABA using SELA for aircraft flyovers and SELC for booms were not in agreement with results obtained in the present study.

Leatherwood, Jack D.↗
NASA NTRS2018

Using Machine-Learning Methods and Expert Prediction Probabilities to Forecast Solar Flares

It has long been known that studying connection between solar flares and properties of magnetic field in active regions is very important for understanding the flare physics and developing space weather forecasts. The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) obtains tremendous amounts of magnetic field data products. However the operational NOAA Space Weather Prediction Center (SWPC) forecasts of solar flares still represent prediction probabilities issued by the experts. In this research we investigate the possibilities to enhance the daily operational flare forecasts performed at the SWPC by developing a synergy of the expert predictions and physics-based criteria, and by employing machine-learning methods. Among the physics-based criteria we consider the descriptors of the Polarity Inversion Line (PIL) and Space weather HMI Active Region Patches (SHARP), and derive from them daily characteristics of the entire Sun. We also consider the daily descriptors of the GOES Soft X-Ray (SXR) 1-8 Angstroms flux such as the flare history of the previous days and averaged X-Ray flux. We estimate the effectiveness in separation of flaring and non-flaring cases for each characteristic, as well as for the expert prediction probabilities, and find that some PIL, SHARP and SXR descriptors are as effective as the expert prediction probabilities and should be considered to issue the flare forecast. Finally, we train and test several Machine-Learning classification algorithms (Support Vector Classifiers with various kernel functions, k-Nearest Neighbor Classifier, Random Forest Classifier, and Neural Networks) using the most effective descriptors and expert prediction probabilities, and compare the obtained predictions with the current SWPC forecasts.

Machine-Learning↗
DOE OSTI2022

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning-based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non-negative matrix factorization to enable learning of a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub-images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. Importantly, this yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Metal–Nitrogen–Carbon Cluster–Decorated Titanium Carbide is a Durable and Inexpensive Oxygen Reduction Reaction Electrocatalyst

Clusters of nitrogen- and carbon-coordinated transition metals dispersed in a carbon matrix (e. g., Fe-N-C) have emerged as an inexpensive class of electrocatalysts for the oxygen reduction reaction (ORR). In this work, it was shown that optimizing the interaction between the nitrogen-coordinated transition metal clusters embedded in a more stable and corrosion-resistant carbide matrix yielded an ORR electrocatalyst with enhanced activity and stability compared to Fe-N-C catalysts. Utilizing first-principles calculations, an electrostatics-based descriptor of catalytic activity was identified, and nitrogen-coordinated iron (FeN 4 ) clusters embedded in a TiC matrix were predicted to be an efficient platinum-group metal (PGM)-free ORR electrocatalyst. Guided by theory, selected catalyst formulations were synthesized, and it was demonstrated that the experimentally observed trends in activity fell exactly in line with the descriptor-derived theoretical predictions. The Fe-N-TiC catalyst exhibited enhanced activity (20 %) and durability (3.5-fold improvement) compared to a traditional Fe-N-C catalyst. It was posited that the electrostatics-based descriptor provides a powerful platform for the design of active and stable PGM-free electrocatalysts and heterogenous single-atom catalysts for other electrochemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Predicting CaO-(MgO)-Al2O3-SiO2 glass reactivity in alkaline environments from force field molecular dynamics simulations

In this investigation, force field-based molecular dynamics (MD) simulations have been employed to generate detailed structural representations for a range of amorphous quaternary CaO-MgO-Al{sub 2}O{sub 3}-SiO{sub 2} (CMAS) and ternary CaO-Al{sub 2}O{sub 3}-SiO{sub 2} (CAS) glasses. Comparison of the simulation results with select experimental X-ray and neutron total scattering and literature data reveals that the MD-generated structures have captured the key structural features of these CMAS and CAS glasses. Based on the MD-generated structural representations, we have developed two structural descriptors, specifically (i) average metal oxide dissociation energy (AMODE) and (ii) average self-diffusion coefficient (ASDC) of all the atoms at melting. Both structural descriptors are seen to more accurately predict the relative glass reactivity than the commonly used degree of depolymerization parameter, especially for the eight synthetic CAS glasses that span a wide compositional range. Hence these descriptors hold great promise for predicting CMAS and CAS glass reactivity in alkaline environments from compositional information.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multiresolution classification of turbulence features in image data through machine learning

During large-scale simulations, intermediate data products such as image databases have become popular due to their low relative storage cost and fast in-situ analysis. Serving as a form of data reduction, these image databases have become more acceptable to perform data analysis on. In this work, we present an image-space detection and classification system for extracting vortices at multiple scales through wavelet-based filtering. A custom image-space descriptor is used to encode a large variety of vortex-types and a machine learning system is trained for fast classification of vortex regions. By combining a radial-based histogram descriptor, a bag of visual words feature descriptor, and a support vector machine, our results show that we are able to detect and classify vortex features at various sizes at multiple scales. Once trained, our framework enables the fast extraction of vortices on new, unknown image datasets for flow analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon

Improved understanding of structural and chemical properties through local experimental probes, such as X-ray absorption near-edge structure (XANES) spectroscopy, is crucial for the understanding and design of functional materials. In recent years, significant advancements have been made in the development of data science approaches for the automated interpretation of XANES structure–spectrum relationships. However, existing studies have primarily focused on crystalline solids and small molecules, while fewer efforts have been devoted to disordered systems. Thus, in this work, we demonstrate the development of neural network models for predicting and interpreting XANES spectra of amorphous carbon (a-C) from local structural descriptors. Comparison between different structural descriptors expectedly shows that the inclusion of both bond length and bond angle information is necessary for an accurate prediction of the spectra. Among the descriptors considered in this work, we find that the local many-body tensor representation yields the highest accuracy and greatest interpretability so that it can be leveraged to understand the importance of structural motifs in determining XANES spectra. Furthermore, we also discuss performance of neural network models for predicting both local structure features, such as bond lengths and bond angles, and global chemical composition, such as the sp:sp 2 :sp 3 ratio.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Revealing Local and Directional Aspects of Catalytic Active Sites by the Nuclear and Surface Electrostatic Potential

This work examines the prospects of using the electrostatic potential, V(r), as a descriptor in heterogeneous catalysis. In particular, the subatomic spatial resolution of the property allows for analysis of both directionality and confinement effects in surface adsorption. This feature of V(r) is used to identify adsorption sites, orientations, and energetics for metal surfaces, particles, and nanoclusters upon interactions with catalytically relevant intermediates. The use of V(r) in assessing the 3D nature of catalytic sites in low-temperature and electrocatalysis is highlighted, and future directions in catalysis design are discussed. Ultimately, we provide a critical analysis of the use of V(r) in the predictions of local adsorption susceptibilities, and we address its limitations. The link between V(r) and other established descriptors in catalysis are motivated via physical relations and theoretical derivations; close ties are established between V(r) and the d-band center (ε d ), as well as the surface site stability (BE M ). In conclusion, by comparing the performance of V(r) evaluated on isodensity contours, i.e., the surface electrostatic potential, to that of V(r) evaluated at the nucleus of an atom, we investigate the application space for a directional and an atom-localized version of the V(r) descriptor for catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Beyond Thermodynamics: Assessing the Dynamical Softness of Hydrated Ions from First Principles

Ion (de)hydration is a key rate-determining step in interfacial processes from corrosion to electrochemical energy storage. However, predicting the kinetics of ion (de)hydration remains challenging, prompting the use of static proxies such as hydration energy and valence. While useful for assessing thermodynamic preferences, such descriptors cannot fully capture the dynamical softness of the hydration shell that dictates kinetics. Accordingly, we use first-principles molecular dynamics to analyze hydration shell softness for a diverse set of metal cations. Additionally, three dynamic metrics are introduced to intuitively describe the bond rigidity, shape deformability, and exchange fluidity of the solvation shell. Together, these metrics capture the relevant physics in the static descriptors, while offering a far more complete and efficient representation for the overall propensity for (de)hydration. Application to the hydrated ion set demonstrates a weak connection between dynamical softness and hydration energy, confirming that dynamical descriptors of hydration are key for correctly describing ion transfer processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Corrosion and Enhanced Hydrogen Evolution in Electrochemical Reduction of Ammonium Carbamate on Transition Metal Surfaces

Experiments and theory are combined to search for catalyst activity and stability descriptors for the direct reactive capture and conversion (RCC) of CO 2 in ammonia capture solutions using Cu, Ag, Au, Sn, and Ti electrodes. Two major phenomena emerge in RCC that are not predominant in the electrochemical CO 2 reduction (CO 2 R) reaction, namely, the rapid corrosion and restructuring of the catalyst in the presence of the CO 2 -ammonia adducts and the promotion of the competing hydrogen evolution reaction (HER). The prevalence of HER in RCC is correlated to the electrostatic attraction of the protonated amine to the electrode and the repulsion of the captured CO 2 , using the potential of zero charge (PZC). The stability of catalysts under RCC conditions is a function of the applied potential and cannot be readily predicted using binding energy descriptors commonly used in the prediction of CO 2 R activity. Finally, a direct correlation between calculated binding energies of CO 2 R intermediates, atomic oxygen, hydrogen, and ammonia and the activity and stability of transition metals for RCC cannot be found, highlighting the need for descriptors beyond those known for CO 2 R.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

A mechanistic understanding of metal–organic framework (MOF) synthesis and scale-up remains underexplored due to the complex nature of the interactions of their building blocks. In this work, we investigate the collective assembly of building units at the early stages of MOF nucleation, using MIL-101(Cr) as a prototypical example. Using large-scale molecular dynamics simulations, we observe that the choice of solvent (water and N,N-dimethylformamide), the introduction of ions (Na+ and F–) and the relative populations of MIL-101(Cr) half-secondary building unit (half-SBU) isomers have a strong influence on the cluster formation process. Additionally, the shape, size, nucleation and growth rates, crystallinity, and short and long-range order largely vary depending on the synthesis conditions. We evaluate these properties as they naturally emerge when interpreting the self-assembly of MOF nuclei as the time evolution of an undirected graph. Solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly. While pure solvents lead to the rapid formation of a small number of large clusters, the presence of ions in aqueous solutions results in smaller clusters and slower nucleation. This diversity is captured by the key features of the graph representation. Principle component analysis on graph properties reveals that only a small number of molecular descriptors is needed to deconvolute MOF self-assembly. Furthermore, descriptors such as the average coordination number between half-SBUs and fractal dimension are of particular interest as they can be can be followed experimentally by techniques like by time-resolved spectroscopy. Ultimately, graph theory emerges as an approach that can be used to understand complex processes revealing molecular descriptors accessible by both simulation and experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

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

Electronic structure prediction of medium and high entropy alloys across composition space

We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

materials science↗
DOE OSTI2023

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗