Shah Research Group

Ionic Conductivity of Ionic Liquid: Nernst-Einstein and Einstein Formalism

Our research on the ionic conductivity of ionic liquids focuses on developing accurate and computationally efficient methodologies to predict transport properties critical for applications in energy storage and electrochemical systems. While the widely used Nernst–Einstein formalism provides a convenient estimate of ionic conductivity, it neglects ion–ion correlations and is therefore inadequate for concentrated systems such as pure ionic liquids. To address this limitation, we employ the more rigorous Einstein formalism within molecular dynamics simulations to explicitly capture correlated ionic motion. Our work systematically identifies key challenges associated with this approach, including the need for long simulation times to achieve convergence and the sensitivity of conductivity predictions to trajectory length and sampling frequency. We establish a robust computational workflow that ensures reliable estimation of ionic conductivity, while also introducing strategies to reduce computational cost, such as optimized trajectory sampling and alternative formulations based on system dipole moments. These efforts provide fundamental insights into ion transport mechanisms and offer practical guidelines for the design and simulation of high-conductivity ionic liquids for next-generation battery and electrochemical applications.

Using Ionic Liquid to capture atmospheric CO2

Our research on ionic liquids for CO₂ capture focuses on understanding and engineering the molecular-level interactions that govern gas absorption and reaction in advanced solvent systems. By incorporating ionic liquid (IL) additives into nonaqueous reactive mixtures such as potassium hydroxide and ethylene glycol, we investigate how the heterogeneous redistribution of ions at the gas–liquid interface can be leveraged to enhance CO₂ capture performance. Using molecular dynamics simulations in conjunction with experiments, we systematically examine the role of IL cations, anions, and concentration in tuning key properties such as surface tension, CO₂ solubility, and interfacial chemical environment. Our findings reveal that solvophobic ions preferentially localize at the interface, creating a favorable microenvironment that promotes CO₂ dissolution, transport, and interaction with reactive species such as hydroxide ions. These insights highlight the critical role of interfacial phenomena and ion-specific effects in governing capture efficiency, and provide a framework for the rational design of IL-enhanced solvent systems for next-generation carbon capture technologies.

Rational Design of Novel Biodegradable Ionic Liquids

Our research focuses on the rational design of novel biodegradable ionic liquids (ILs) by integrating molecular-level simulations with principles of chemical and biochemical engineering. While room-temperature ionic liquids offer significant advantages as tunable, nonvolatile solvents for applications in energy, separations, and reaction engineering, their environmental persistence and toxicity present critical challenges. We investigate the fundamental molecular mechanisms governing IL biodegradation, with an emphasis on how structural features—such as alkyl chain length and ion pairing—affect interactions with enzymatic systems like cytochrome P450. Using a combination of molecular docking, molecular dynamics simulations, and free energy calculations, we identify key interactions, binding conformations, and active-site residues that control the initial steps of enzymatic transformation. These insights enable the design of ionic liquids with enhanced susceptibility to biodegradation, while also guiding the development of engineered biocatalysts for efficient remediation. Through this approach, our work aims to bridge the gap between molecular design and environmental sustainability, advancing the development of next-generation green solvents.

Accelerating Ionic Liquid Discovery through Machine Learning

Our research on machine learning for ionic conductivity prediction focuses on developing data-driven frameworks to accelerate the discovery and design of high-performance ionic liquids for energy storage applications. Leveraging large datasets of experimentally measured conductivities across diverse cation–anion combinations and temperature ranges, we employ advanced machine learning techniques—including artificial neural networks, support vector regression, random forest, and gradient boosting—to establish robust structure–property relationships. These models capture complex, nonlinear dependencies between molecular features and ionic transport behavior, enabling accurate prediction of conductivity over several orders of magnitude. Beyond prediction, interpretability tools are used to identify key molecular descriptors governing ionic conductivity, providing physical insight into ion transport mechanisms. The trained models are further used to screen vast chemical spaces of hypothetical ionic liquids and mixtures, uncovering candidates with enhanced conductivity and revealing non-ideal mixing effects. This integrated approach bridges molecular design and data science, offering a powerful pathway for the rapid development of next-generation electrolyte materials.