Predicting Density and Elucidating the Thermodynamic Drivers of Viscosity in Carboxy-Functionalized Imidazolium Ionic Liquids

Development of United-Atom Force Fields for Monomeric and Oligomeric Ionic Liquids through Regression-Guided Optimization of Electronic Continuum Correction Parameters

Ionic liquids are salts that exist in the liquid state at room temperature and exhibit high viscosity because of their strong electrostatic interactions. It was difficult to reproduce their viscosity by molecular dynamics simulations with conventional nonpolarizable force fields; however, recent development of force fields implementing electronic continuum correction (ECC), which accounts for polarizability, has enabled accurate predictions. Here, we present a regression-guided strategy to optimize scaling factors for ECC charges and Lennard-Jones parameters for monomeric (BMIM+, MOEMIM+) and oligomeric (IL22+, IL44+) imidazolium-based cations paired with TFSI– with united-atom models. The scaling factors were optimized to simultaneously reproduce experimental density and viscosity. To validate the strategy, we calculated the temperature dependence of density, diffusion coefficient, conductivity, and viscosity of BMIM–TFSI, achieving good agreements with experiments. Moreover, scaling factors optimized for MOEMIM+, which shares similar chemical structure and elemental compositions with IL22+ and IL44+, were found to be transferable to these compounds. Thus, this work not only provides a practical regression-guided workflow for selecting ECC-based united-atom force-field parameters but also suggests their transferability across chemically related ionic liquids.

Predicting Properties of Key Ingredients of Battery Solvents with Density Functional Theory and Adaptive Force Matching

The performance of batteries is heavily influenced by the properties of their solvents, which play a crucial role in ion solvation, conductivity, and electrochemical stability. However, traditional force field models often fall short in accurately predicting these properties. This study investigates the potential of adaptive force matching (AFM) to predict a range of solvent-related properties using only electronic structure theory. To demonstrate this approach, we developed AFM models based on B3LYP-D3(BJ) for three common molecules present in battery solvents: dimethylamine (DMA), dimethyl carbonate (DMC), and tetrahydrofuran (THF). Our results reveal that AFM models significantly outperform traditional force fields, including OPLS-AA (CM1A), GAFF2, and GROMOS 54A7, in predicting key properties such as density, heat of vaporization, viscosity, diffusion constant, boiling temperature, and free energy of vaporization. Notably, the average percentage error of AFM models is approximately 13%, substantially lower than that of traditional force fields (21% for GAFF2, the best-performing empirical model). These findings underscore the promise of AFM in predicting physical properties of battery solvents, with far-reaching implications for chemistry, materials science, and related fields where accurate predictions are essential for understanding complex phenomena and designing innovative materials and systems.

Data-Driven Exploration of Weakly Solvating Additives toward the Stabilization of Zn Metal Anodes

The introduction of weakly solvating additives (WSAs) into aqueous electrolytes holds significant potential in promoting the desolvation kinetics of Zn2+ and reducing polarization. However, the current strategy of WSA screening primarily depends on trial-and-error experiments and theoretical calculations. This study reveals that, among various molecular features, the electrostatic potential minimum (ESPmin) exhibits the strongest correlation with the desolvation activation energy (Ea), demonstrating that ESPmin could serve as an effective descriptor for screening WSAs. Herein, we propose a data-driven screening strategy based on ESPmin, in which ESPmin can be accurately predicted directly from molecular structure using a graph convolutional neural network (GCN) model. Assisted by this strategy, several promising WSAs, including tetrahydropyran, methanol, and acetone, were successfully identified. As a proof of concept, tetrahydropyran was selected as the additive for systematic studies. Remarkably enhanced cycling stability with a lifespan of over 2400 h and low overpotential was demonstrated for the symmetric cell with tetrahydropyran. The proposed data-driven strategy enables the rapid screening of WSAs directly from molecular structures, offering an efficient pathway for the exploration of high-performance electrolyte additives.

Advanced Computational Modeling of CO2 Solubility in Ionic Liquids Using an XAI Methodology

The heightened global imperative for climate change mitigation necessitates the adoption of highly efficient carbon capture and storage (CCS) technologies. While Ionic Liquids (ILs) offer highly modifiable physicochemical properties for selective CO2 absorption, an accurate, high-performance prediction of CO2 solubility across diverse operating conditions remains a significant computational challenge. This research addresses this predictive constraint by developing a high-fidelity Quantitative Structure-Property Relationship (QSPR) model, realized through an Artificial Neural Network (ANN) topology, to forecast the degree of CO2 solubility across 14 different IL chemical families and a wide thermodynamic range (9.7–100120 KPa, 298–373 K). The developed QSPR model achieved exceptional predictive results (R2 = 0.989; MSE = 5.41e-4). Three innovations distinguish this work for real-world deployment: First, the model is presented in a "white-box" framework, which is critical for enhancing reproducibility and enabling swift integration into commercial software apps. Second, an explainable AI (XAI) methodology, utilizing Garson's algorithm, provided essential mechanistic insights, establishing dominance of the system's input features in the order: pressure > critical temperature > molecular weight > acentric factor > critical pressure > temperature. This analysis guides future solvent synthesis by confirming the primary thermodynamic control over the absorption process. Third, the model demonstrates an ultra-low computational footprint, requiring only 33 multiply-accumulate operations (MAC) and consuming 264 bytes of memory, making it highly suitable for low-latency, real-time prediction within digital twin environments. Given the critical role of CO2 solubility in solvent selection for carbon capture, this research is particularly relevant for the oil and gas industry, offering a tool to enhance natural gas quality by removing CO2, improving calorific value, and reducing pipeline corrosion.

Machine learning-assisted QnSPR study of structurally diverse nanoporous carbon materials and their capacitive behavior in dilute ionic liquid electrolyte

Active Learning for Optimizing Adsorption Energy Predictions in Large Chemical Spaces

Optimizing catalysis requires the efficient exploration of immense chemical spaces, particularly for high-entropy (multielement) systems where properties depend on complex variables like geometry, composition, and site-specific interactions. In this work, we demonstrate a global active learning framework to map these landscapes efficiently. By coupling genetic algorithms with deep neural networks trained on density functional theory data, our approach learns the potential energy surface while optimizing chemical structures simultaneously, bypassing costly density functional theory relaxations. We apply this framework to predict H2, N2, and NH3 adsorption energies on 10–50 atom clusters composed of Ag, Au, Cu, Ni, Pd, and Pt. The model achieves a mean absolute error of less than 0.10 eV against density functional theory validation. Results identify regions with moderate H2 physisorption (−0.1 to −0.5 eV) and other regions with strong chemisorption (−1.0 to −2.0 eV). Physisorption mainly consists of adsorption on Ag, Au, Ni, and Cu, while chemisorption involves Pd and Pt sites. High-entropy environments can provide a diverse distribution of local chemical conditions. This site variance is critical for multistep reactions where intermediate steps possess conflicting optimal binding energies that cannot be simultaneously satisfied by low-entropy surfaces, thereby offering entropy as a tunable parameter for catalyst design. This framework provides a scalable and resource-efficient strategy for high-throughput materials discovery in applications such as hydrogen storage and ammonia synthesis.