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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantifying the anion effect of gas solubility within ionic liquids using the solvation affinity index

The solubility of gas molecules within ionic liquids (ILs) is strongly dictated by the anion effect and the free volume effect, and of these two influences, the anion effect can be challenging to predict. We evaluate a wide range of different gas solutes (O 2 , NH 3 , CH 4 , H 2 S, SO 2 , Ar, H 2 , N 2 , CO 2 ) in different ILs, and we find that the solubility is strongly correlated to one of three different IL properties, which can be directly calculated from the anion electrostatic potential (ESP) surfaces. Thus, the solutes can be grouped into three fundamental types, leading to a general descriptor, the solvation affinity index (SAI), which can identify the affinity between a wide range of gas solutes and IL solvents. The SAI of nine common industrial gases with 47 common anions are generated. Furthermore, this can provide a fast approach for pre-screening IL solvents or even for ILs embedded within porous supports.

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

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant–solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water–cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

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

Screening of metal complexes and organic solvents using the COSMOSAC-LANL model to enhance the energy density in a non-aqueous redox flow cell: an insight into the solubility

In this paper, we have proposed a first-principles methodology to screen transition metal complexes against a particular organic solvent and organic solvents against a particular transition metal complex based on their solubility information without the knowledge of heat of fusion and melting temperature. The energy density of a non-aqueous redox flow cell directly depends on the solubility of the redox active species in the non-aqueous medium. Here, we have used the “COSMOSAC-LANL” activity coefficient model which is based on first-principles COSMO calculations where the microscopic information is passed to the macroscopic world via a dielectric continuum solvation model, followed by a post-statistical thermodynamic treatment of the self-consistent properties of the solute particle to calculate the solubility. To model the activity coefficient at infinite dilution for the binary mixtures, a 3-suffix Margules (3sM) function is introduced for the quantitative estimation of the asymmetric interactions and, for the combinatorial term, the Staverman–Guggenheim (SG) form is used. The new activity coefficient model is separately called the “LANL” activity coefficient model. The metal complex and the organic solvent have been treated as a simple binary mixture. The present model has been applied to a set of 14 different organic solvents and 16 different transition metal complexes. Using the new LANL activity coefficient model in combination with the ADF-COSMOSAC-2013 model, we have shown how one can improve the solubility of a transition metal complex in an organic solvent. We applied our model to screen 84 binary mixtures to predict the compatible pair of redox active species and organic solvent to increase the energy density. The solvation mechanism of the transition metal complexes in the organic solvents was obtained using the new model. The results have been compared with the experimental and theoretical results where they are available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

09 BIOMASS FUELS↗
DOE OSTI2026

Rapid Evaluation of Amine-Functionalized Solvents for Biomass Deconstruction Using High-Throughput Screening and One-Pot Enzymatic Saccharification

Efficient and sustainable pretreatment of lignocellulosic biomass is critical for biofuel and biochemical production, yet its optimization is often hindered by slow, labor-intensive experimental methods. Here, we report the first demonstration of a custom-built, miniaturized, high-throughput screening platform integrated with one-pot enzymatic saccharification, enabling parallel evaluation of solvent type, feedstock, and temperature with minimal material use and high reproducibility. As a proof-of-concept, the HTX platform was used to screen five amine-functionalized solvents, including isopropanolamine, butylamine, N-methylbutylamine, ethanolamine, and ethanolamine acetate across three bioenergy crops (sorghum, poplar, and switchgrass) and pretreatment temperatures ranging from 80 to 140 °C. Vacuum drying successfully removed more than 99% of the solvents from the pretreated biomass, eliminating the need for water washing prior to saccharification. Isopropanolamine and N-methylbutylamine yielded the highest glucose (70–80%) and xylose (58–67%) release, with trends reflecting feedstock recalcitrance. The produced hydrolysates supported robust growth of an engineered strain of the yeast Rhodosporidium toruloides, confirming biocompatibility. This high-throughput platform provides a scalable, feedstock-agnostic framework for rapid pretreatment screening, accelerating solvent–feedstock pairing and process optimization. Its ability to integrate pretreatment, solvent removal, saccharification, and microbial conversion in a miniaturized format offers significant advantages for cost-competitive biorefinery development.

Biomass↗
DOE OSTI2025

Mechanistic Insights into the Demethylation of Lignin‐Derived Structures Using Protic Ionic Liquids: A Density Functional Theory Study

Lignin valorization is restricted by the stability of its methoxy groups, creating a critical need for efficient demethylation strategies. Here, in this study, density functional theory (DFT) is employed to dissect the mechanistic pathways of demethylation in lignin model compounds, guaiacol and syringol, using protic ionic liquids (PILs) that act as both solvent and catalyst. Conductor‐like Screening Model for Real Solvents (COSMO‐RS) analysis identifies monoethanolammonium acetate ([MEOA][Ace]) as the most promising medium, attributed to its strong hydrogen bonding network and solvation ability. By integrating implicit and explicit solvation models, it is revealed that an acid‐catalyzed hydrolytic mechanism governs demethylation, with PILs stabilizing crucial transition states and intermediates. Complementary electronic structure evaluations, including highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO‐LUMO) gap analysis, charge distribution, and electrostatic potential mapping, demonstrate how PILs lower energetic barriers and enhance reactivity. To simulate realistic environments, this study is extended to lignin dimer complexes with varying water content, uncovering how water‐bridged solvation reverses demethylation preference from guaiacyl to syringyl units. This mechanistic shift aligns with experimental observations showing faster S‐unit reactivity in hydrated systems. Together, these findings provide atomic‐level insight into lignin demethylation dynamics and highlight how tuning acid concentration in PILs can accelerate kinetics, enabling a rational pathway toward next‐generation biomass conversion technologies.

COSMO-RS↗
DOE OSTI2022

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

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

Computational Approach for Rapidly Predicting Temperature–Dependent Polymer Solubilities Using Molecular–Scale Models

One promising approach to recycle multicomponent plastic waste (e. g., multilayer plastic films) is selective dissolution. Selective dissolution is a solvent-mediated process in which differences in polymer solubility in a carefully chosen solvent system are exploited to recover a target polymer. Here, a computational approach was developed that rapidly predicts temperature-dependent polymer solubilities to guide the design of solvent systems for solvent-mediated polymer recycling. Polymer conformations were obtained from molecular dynamics simulations by modeling the polymer as a short oligomer and then used as input to the conductor-like screening model for real solvents (COSMO-RS) for solubility predictions. Using polyethylene (PE) and ethylene vinyl alcohol (EVOH) as representative polymers, the effect of simulation parameters was systematically studied, and predicted solubilities were found to be in good agreement with experimental measurements. Furthermore, the applicability of the approach was demonstrated by identifying selective solvents for PE and EVOH dissolution from a library of 524 solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Heuristic Computational Model for Predicting Lignin Solubility in Tailored Organic Solvents

Lignin is a random heteropolymer that has been extensively studied as a renewable source of aromatic precursors for high-value chemicals, biofuels, and bioplastics. A key challenge in lignin valorization is the structural and compositional heterogeneity of lignin feedstocks. Solvent-based approaches are commonly used to fractionate lignin to reduce this heterogeneity, but solvent selection can be challenging due to variability in lignin composition. In this work, we developed computational methods to predict good and poor organic solvents as a function of lignin composition. We analyzed 28 different linear pentamer structures, 18 from known libraries and 10 hypothetical polymers, and calculated their activity coefficients in 50 different organic solvents by using the conductor-like screening model for realistic solvents. We used these data to train a regression model that enabled the extensive investigation of the impact of solvent and monolignol compositions on predicted lignin solubility. The exhaustive exploration of solubility trends using model predictions revealed sets of solvents, identified using Kamlet–Taft parameters, that are predicted to promote lignin dissolution regardless of lignin composition. We further identified solvents expected to selectively isolate lignin fractions enriched in certain subunits. Furthermore, these results establish heuristic guidelines for solvent selection that can be used to tailor fractionation processes for lignin feedstocks of distinct composition or to design new processes that isolate fractions with higher proportions of selected subunits.

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

Solvent selection for biphasic extraction of 5-hydroxymethylfurfural via multiscale modeling and experiments

We introduce a comprehensive conceptual framework for selecting solvents for reactive extraction in biphasic organic-water systems and demonstrate it for the separation of HMF (5-hydroxylmethylfurfural), a platform chemical produced in the acid-catalyzed dehydration of hexoses. We first perform in silico screening of ~2500 solvents, from the ADFCRS-2018 database using the ADF COSMO-RS implementation, and classification, based on the solvent partition coefficient. We then determine experimentally the partition coefficients for HMF, fructose, and products of HMF rehydration (levulinic acid (LA), and formic acid (FA)), the mutual water-organic solvent solubilities, and the separation factors in >50 select solvents spanning multiple homologous series at room temperature and a typical reaction temperature with in situ sampling. We find that COSMO-RS is excellent for screening purposes (typical error in most cases within a factor of ~2). Increased temperatures lead to significant reduction in partitioning, and room temperature measurements are clearly inadequate for solvent selection. Upon down selecting classes of solvents based on separation performance, we perform experimental thermal stability and reaction compatibility studies of a small set of solvents at relevant reactive-extraction temperatures. We discover that many substituted phenols exhibit an order-of-magnitude increase in partitioning compared to conventional solvents due chiefly to hydrogen bond interactions and show the necessary stability but retain a significant fraction of water and LA, factors that need to be considered in technoeconomic analysis. In contrast, anilines, aldehydes, and acids are good to excellent regarding separation but incompatible with this specific reaction medium. Furthermore, this multifaceted framework can be extended to other biomass-derived products and processes.

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

Chemical Recycling of Mixed Plastics in Electronic Waste Using Solvent-Based Processing

Currently, less than 20% of electronic waste (E-waste) produced in the U.S. is recycled. To improve the recycling rate of E-waste, the study aimed to: (1) identify the major plastics found within electronic shredder residue (ESR), (2) design solvents and processing conditions capable of separating out 90% of the plastic in ESR, and (3) estimate the energy efficiency of the solvent-based process developed. Preliminary screening showed 25 wt.% of the ESR was composed of plastics, with two polymers dominating the sorted plastic fraction—polystyrene (PS, 40 wt.%) and acrylonitrile butadiene styrene (ABS, 25 wt.%). Subsequently, solvents and anti-solvents were screened using Hansen Solubility Parameter Theory for PS, ABS, and ESR dissolution. The pre-screening results showed dichloromethane (DCM) and tetrahydrofuran (THF) as the most effective solvents for PS and ABS, with methanol (MeOH) and ethylene glycol (EG) as the most effective anti-solvents. By optimizing the dissolution time and the solvents used, the highest polymer dissolution yield (99 wt.%) was achieved using DCM for 48 h. Both MeOH and EG precipitated 71 wt.% of the polymer fraction of ESR. EG removed more phosphorus containing flame retardants (94 wt.%) than MeOH (69 wt.%). Energy analysis indicated that the solvent-based processes could save 25–60% of the embodied energy for PS and ABS. Characterization showed that the solvent-based processing could preserve the high molecular weight fraction of the polymers while removing flame retardants at the same time. The results from this study prove the potential of solvent-based processing to produce secondary plastic materials from E-waste for cross-industry reuse.

Anderson, Lester↗
DOE OSTI2020

Charge regulation mechanism in end-tethered weak polyampholytes

Weak polyampholytes, containing oppositely charged dissociable groups, are expected to be responsive to changes in ionic conditions. Here, we determine structural and thermodynamic properties, including the charged groups’ degrees of dissociation, of end-tethered weak polyampholyte layers as a function of salt concentration, pH, and the solvent quality. For diblock weak polyampholytes grafted by their acidic blocks, we find that the acidic monomers increase their charge while the basic monomers decrease their charge with decreasing salt concentration for pH values less than the pK a value of both monomers and vice versa when the pH > pK a . This complex charge regulation occurs because the electrostatic attraction between oppositely charged blocks is stronger than the repulsion between monomers with the same charge in both good and poor solvents when the screening by salt ions is weak. This is shown by the retraction of the top block into the bottom layer. In the case of poor solvent conditions to the basic block (the top block), we find lateral segregation of basic monomers into micelles, forming a two-dimensional hexagonal pattern on the surface at intermediate and high pH values for monovalent salt concentrations from 0.01 to 0.1 M. When the solvent is poor to both blocks, we find lateral segregation of the grafted acidic block into lamellae with longitudinal undulations of low and high acidic monomer density. By exploiting weak block polyampholytes, our work expands the parameter space for creating responsive surfaces stable over a wide range of pH and salt concentration.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tuning and high throughput experimental screening of eutectic electrolytes with co-solvents for redox flow batteries

Eutectic solvents, with high salt concentrations and suppressed volatility, are promising alternatives to aqueous and volatile organic electrolytes for grid-scale energy storage devices such as redox flow batteries (RFBs). However, it is not known a priori which hydrogen bond acceptors and donors would form a eutectic solvent and at which compositions of these mixtures would yield desirable properties for RFBs. Specifically, the known deep eutectic solvents generally have high viscosities and low ionic conductivities, especially in the landscape of RFB electrolytes. We report a high throughput experimental (HTE) study was carried out to assess the properties of eutectic solvents based on choline chloride as the hydrogen bond acceptor and ethylene glycol and aniline as the hydrogen bond donors. In addition, water, acetonitrile, and dimethyl sulfoxide as co-solvents were examined in terms of tuning the viscosity, conductivity, and solubility of two redox-active species: methyl viologen dichloride hydrate (MVCl 2 ·H 2 O, or MV for simplicity) and 2,1,3-benzothiadiazole (BTZ) that are relevant to RFBs. The HTE framework enabled the development of a rapid and comprehensive understanding of the design of new eutectic electrolytes incorporating co-solvents to improve the physicochemical and electrochemical properties for RFBs.

25 ENERGY STORAGE↗
DOE OSTI2020

Reversible redox chemistry in azobenzene-based organic molecules for high-capacity and long-life nonaqueous redox flow batteries

Redox flow batteries (RFBs), often categorized as aqueous and nonaqueous systems, represent a type of large-scale energy storage technology for renewable energy resources. Redox-active organic molecules have recently drawn extensive interests in RFBs as promising active materials due to their elemental abundance, structural diversity and high tunability of properties, but employing organic molecules in nonaqueous systems is far limited in terms of useable capacity and cycling stability. Here we introduce a class of azobenzene-based organic compounds with the azo group as redox-active center in the p-conjugated structure as new active materials to realize high-performance nonaqueous RFBs with long cycling life and high capacity. By screening suitable organic solvents with high solubility and conductivity, the azobenzene organic molecule was capable to achieve a stable long cycling with a low capacity decay of 0.014% per cycle and 0.16% per day over 1000 cycles. And the stable cycling of electrolytes under a high concentration of 1 M was also realized, delivering a high reversible capacity of ~46 Ah L -1 . The unique lithium-coupled redox chemistry accompanied with a voltage increase was observed and revealed by experimental characterization and theoretical simulation. With the reversible two-electron redox activity of azo group in p-conjugated structures, azobenzene as an azo-aromatic molecule represents a class of promising redox-active organics for potential grid-scale energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Inhibition of asphaltene aggregation using deep eutectic solvents: COSMO-RS calculations and experimental validation

Asphaltene precipitation/deposition adversely affects various oil and gas processes, such as oil recovery, transportation, and petroleum processing. The resulting increase in viscosity of crude oil decreases distillate yields creating unstable phase separation. Deep eutectic solvents (DESs) have recently gained interest as inhibitors of asphaltene aggregation. The goal of the present study is to derive the mechanism of inhibition of petroleum asphaltene aggregation by a novel screening of DESs. An archipelago-based chemical structure of asphaltene was adopted for performing quantum chemical calculations. The structure was used in the conductor-like screening model for real solvents (COSMO-RS) model to screen potential DESs for asphaltene precipitation. It was found that DESs containing thymol were the most promising of the 153 DES combinations screened. The COSMO-RS predictions were validated experimentally using solubility data of asphaltene in DESs. Among the studied DESs, thymol-diphenyl ether provided the highest solubility for asphaltene, which was further validated using the experimental and the COSMO-RS predicted data. In addition, to characterize the structural interactions between asphaltene and DESs, Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) measurements were performed. It was found that strong interaction between asphaltene and the DESs is responsible for the higher asphaltene dispersion. Finally, the present approach opens pathways to rationally design and understand the impact of structural variation of DESs based on their interactions with asphaltene.

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

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst–Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

25 ENERGY STORAGE↗
DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2023

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗