I, Kalith Mohamed Ismail, am a predoctoral researcher working at the intersection of neural network-based deep learning prediction and electronic structure theory-based calculations.

My work aims to understand inelastic neutron spectroscopy results of organic crystal structures, providing deeper insights into their thermophysical properties. Additionally, I develop hybrid high-performance computing (HPC) solutions to handle large-scale computational and optimize computational efficiency. By integrating advanced HPC techniques with cutting-edge neural network models, my research bridges the gap between theoretical physics and computational innovation, contributing to a deeper understanding of material properties and their real-world applications.