Instructor

The petitioner proposes to develop advanced machine learning methods, cheminformatics tools, and modeling techniques to accurately predict molecular and chemical reaction properties. This work aims to accelerate the discovery of new molecules and streamline screening processes for pharmaceuticals, cosmetics, biofuels, and industrial dyes. The endeavor focuses on integrating predictive algorithms with cheminformatics models to assess novel molecules with high speed and accuracy.

The petitioner proposes to continue research in computational chemistry, specifically focusing on the development of computational models to understand interfacial phenomena. This work involves the integration of molecular dynamics, quantum chemical modeling, and machine learning to accelerate the discovery of sustainable materials. The endeavor aims to optimize surface interactions and polarization effects to enhance ion rejection and energy conversion efficiency.

The petitioner proposes to continue his research at the intersection of quantum chemistry simulations, machine learning, and big data analytics to accelerate drug discovery and personalize medical treatments. His work focuses on creating precise molecular models that shorten the pharmaceutical development cycle and tailor therapies to individual genetic profiles. Additionally, he intends to apply advanced forecasting models to support environmental sustainability and resource management.

The petitioner proposes to advance and optimize multireference quantum embedding methods and quantum algorithms to enhance the simulation of complex chemical systems. This work focuses on improving computational accuracy for applications in energy, catalysis, and materials science.