research

Hybrid DFT-ML Approach for Organic Energy Storage Materials

Addressing the growing energy demands and environmental concerns necessitates the search for new materials with improved performance for energy storage applications. Organic materials offer a promising alternative to conventional inorganic materials due to their structural versatility, low cost, and environmental sustainability. This project aims to develop a hybrid density-functional theory (DFT) and machine learning (ML) approach to efficiently discover and characterize organic materials for energy storage applications, specifically focusing on organic electrode materials. By training ML models on a DFT-generated dataset, we can predict redox potentials of new candidate materials using easily obtainable structural and electronic input features, significantly accelerating the discovery of novel organic materials with desirable electrochemical properties.

Perovskite Materials for Optoelectronic Applications

Perovskite materials have garnered significant attention due to their remarkable optoelectronic properties, making them promising candidates for applications in solar cells, light-emitting diodes, and other optoelectronic devices. However, challenges such as spectral instability and understanding the factors that control their bandgap need to be addressed. This project aims to investigate perovskite materials for optoelectronic applications, with a focus on understanding and controlling their bandgap and spectral stability. Through density functional theory (DFT) calculations and machine learning techniques, we analyze the factors that influence bandgap tunability and identify strategies to suppress halide migration, which is a major cause of spectral instability, ultimately paving the way for the development of stable perovskite optoelectronics with tailored properties.

Beyond Li-ion Batteries

The search for sustainable and efficient energy storage solutions is essential for addressing the increasing global energy demands. In pursuit of alternatives to Li-ion batteries, this project focuses on carbon quantum dots (CQDs) and covalent organic frameworks (COFs) for energy storage applications. These organic materials can accommodate a variety of ions, including Li, Na, and K, offering the potential for higher energy density and reduced resource dependence. We investigate these organic electrode materials using density functional theory (DFT) calculations and molecular dynamics simulations to gain insights into their electrochemical properties and potential applications in beyond Li-ion batteries, such as Li-air batteries and potassium-ion batteries.

Solid Polymer Electrolyte: Multiscale Modeling of Carbonate-Based Polymer Electrolytes

We focus on developing novel carbonate-based solid polymer electrolytes (SPE) to overcome the limitations of conventional liquid electrolytes, such as flammability, mechanical instability, and electrode dissolution. Utilizing density functional theory (DFT) and molecular dynamics (MD) simulations, we investigate the impact of carbonate pendant group composition on nanophase morphology, Li-ion transport, and glass transition temperature (T_g). Our findings provide crucial insights for designing optimal polymeric systems with enhanced ion transport and safety, paving the way for the next generation of high-performance and reliable energy storage devices.

Metaphotonics: Coded Visibility for Next Generation Battlefield Obscurants

Density functional theory (DFT) and active learning approaches are employed to explore and guide the development of novel materials that fulfill strict military requirements for developing next-generation obscurant systems that offer an asymmetric advantage to warfighters, enhancing their visibility while suppressing adversary visibility and detection. DFT allows for accurate computational predictions of material properties and behaviors, while active learning techniques enable the efficient sampling and optimization of design parameters. Combining these methods, we effectively navigate the vast material design space to identify and optimize materials that meet the demanding performance criteria for military applications. This approach accelerates the development of novel materials, reducing the time and cost associated with experimental trial-and-error methods, ultimately supporting the advancement of military technology.