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  Research Interests

• Theoretical Solvation Science, Machine Learning in Solvation Dynamics
• Theoretical Organic Chemistry: Chemical Bonding, Sulphur Centered Hydrogen Bonds, Halogen bonds, Non-Covalent Interactions
• Structural Investigations, Drug Design, and Machine Learning in Drug Discovery
• Synthesis of Medicinally Active Compounds and Drug Delivery
• Theoretical Modelling of Excited State Phenomena, excited state aromaticity and dynamic aromaticity

What we do?Bridging theory and experiments in chemistry through computations and ML !!

Theoretical and computational chemistry is a powerful branch of science that uses mathematics, physics, and computers to understand the principles behind chemical processes. This field allows scientists to simulate experiments on a computer, giving insights into how molecules interact, form, or change during reactions. It’s like having a virtual laboratory where experiments can happen much faster than in real life, helping to predict the outcomes of real-world experiments before they are conducted. This approach is crucial for developing new drugs, designing materials with specific properties, and understanding environmental processes. It represents a bridge between the abstract world of chemical theory and the tangible benefits of applied chemistry, offering solutions to some of the most pressing challenges of our time.

In the dynamic landscape of modern chemistry, the fusion of theoretical insights with experimental findings through computational methods and machine learning (ML) stands at the forefront of innovation. This interdisciplinary approach not only enhances our understanding of chemical phenomena but also accelerates the discovery of novel materials and pharmaceuticals. By applying sophisticated computational models and ML algorithms, researchers can predict the outcomes of complex chemical reactions, elucidate reaction mechanisms, and tailor the properties of materials with unprecedented precision. This synergy between theory and experiment paves the way for more efficient and sustainable chemical processes, heralding a new era of scientific exploration and technological advancement.

The role of computational chemistry, bolstered by ML, extends beyond mere prediction, acting as a bridge that connects the microscopic world of molecules to macroscopic experimental observations. Through the meticulous simulation of molecular systems and the insightful analysis of large datasets, researchers can uncover patterns and relationships that remain obscured by traditional experimental techniques. This holistic approach not only saves valuable time and resources but also opens up new avenues for research that were previously thought to be beyond our reach. As we continue to harness the power of computations and ML in chemistry, we edge closer to solving some of the most pressing challenges in chemistry of our time, from chemical reactivity and dynamics to potential applications.