Akash Ramdas

Bio

My research focuses on the computational discovery of materials for electronic and energy device applications. I leverage both the physical insights provided by many-body perturbation theory–based methods and statistical inference from open materials databases using machine learning. In my research, I have demonstrated that a multi-objective optimization framework can identify experimentally viable sub-5 nm Cu interconnect alternatives, extracted theoretical insights into unconventional resistivity scaling in NbP from experimental data, significantly improved the accuracy of machine-learned interatomic potentials for moiré reconstructions, accelerated the optimization of catalytic process conditions, and identified materials with exceptionally high inductance at the atomic scale.