CNIS Lab

The self-assembly in an aqueous medium is a major scientific area for many practical and biological applications, in which freezing induced self-assembly (FISA) is considered to be a more novel method as compared to solvent evaporation induced self-assembly. FISA is a versatile and green bottom-up method for producing highly ordered and aligned porous materials. The molecular details of these processes are largely unknown because of the very small length scale involved (nanometres). We have attempted to use seeding methodology to capture the nucleation behaviour of water in the presence of amphiphilic molecules. The understanding has been previously utilized for stationary fluids and sheared flows. However, the underneath algorithm is quite valuable for the current project as well. Crystal nucleation is a rare event that can occur on time scales of seconds, far beyond the reach of the brute-force molecular dynamics framework. Seeding overcomes this barrier by the insertion of ice seed in a pre-equilibrated solution. If the ice seed is larger than the critical nucleus, it will grow with time evolution, leading to the formation of different ice crystals and self-assembly of amphiphilic molecules into 3D or 2D structures. Our work provides preliminary molecular insights into the factors affecting the nucleating behaviour of water in the presence of amphiphilic molecules, which play a pivotal role in designing artificial ice recrystallization inhibition (IRI) agents, for use in cryobiology and drug delivery.

In light of H2 emerging as a promising alternative to conventional fuels, our work focuses on finding suitable g-C3N4 based SAC’s for Hydrogen Evolution Reaction (HER). A three-tier screening based on formation energy, Gibbs free energy and band-gap is used to downselect suitable candidates. Also, a robust machine learning model is built based on the data generated from DFT as well as the features of gas-phase atoms present in the chemical composition. As a part of model development, the performance of Multivariate Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) are compared. In addition, feature importance analysis is employed to identify key descriptors for the HER.

In the global endeavour to achieve carbon neutrality, the anthropogenic CO2 utilization is an integral element that has risen in importance over the past few decades for the production of commodity chemicals. The use of MXene materials towards CO2 capture and conversion has gained intense momentum unlocking several new directions toward CO2 catalytic usage. However, the vast MXene chemical compositions makes it highly difficult to screen over appropriate MXene materials without the guidance of surface chemical and catalytic descriptors. Thus, to discover key properties governing CO2 activation by MXenes using low-cost computational tools are an urgent need. To that end, an in silico machine learning (ML)-assisted strategies can help in identifying potential indicator for CO2 activation for the screening and optimizing catalyst. Herein, we employ density functional theory and ML methodology to find key descriptor by providing a viable pathway for the activation of CO2 over pure and defective MXenes. We also perform feature importance analysis to find key determining, and guiding descriptors for CO2 activation will pave the way for accelerating the search of MXene-based materials and catalysts for CO2 capture and use technologies.

Surfactants are amphiphilic molecules with long hydrophobic chain and hydrophilic polar head, preferentially adsorb at the air/fluid or fluid-fluid interfaces, thereby reduces the surface/interfacial tension. The ability to reduce the surface/interfacial tension makes them ideal candidates for industrial applications such as emulsion and foam stabilizers in cosmetic industry, surface active agents in enhanced oil recovery surface modifiers in agrochemicals and coatings. Typically, nanoparticles (NPs) and inorganic salts are added to such systems to enhance the performance of the surfactants. Introduction of NPs and salt to the surfactant solution alter the adsorption mechanism and thermodynamic properties of ionic surfactant at the air-water interface. This project aims to study the effects of NPs and inorganic salt on the adsorption mechanism and thermodynamic properties of ionic and non-ionic surfactants.

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models or be integrated with them to improve their performance. Our group focuses on the discovery of new materials through large-scale screening for various applications like hydrocarbon capture and storage, and inverse design of materials for any particular application.

Most of the world’s oil is found in carbonate reservoirs which are formed from minerals such as calcite. Only 30-35% of original oil in place can be extracted using the improved oil recovery methods. To increase the oil extraction, enhanced oil recovery (EOR) method is used. EOR uses chemicals (surfactants, alkaline, polymer etc.) to alter the wettability of oil towards the rock surface. Several studies have shown that injecting water with specific ionic composition and concentration into a carbonate reservoir can alter its wettability toward a more water wet scenario, thereby resulting in higher oil recovery. However, a comprehensive explanation and understanding of the wettability alteration mechanism has not emerged. Here, classical molecular dynamics simulations are used to understand the wettability alteration phenomena in presence of inorganic salts at the molecular level.