Research Insterests#
My interests range over high performance computation, computational material science, condensed matter Physics, application of Machine Learning and Quantum Computing in solving models of correlations. My interest in machine learning are a bit more open ended and geared towards looking at statistical side of data and functional side of neural networks, and other real life situations.
ML in Material Science#
After joining Trinity College Dublin, I expanded my research interests over computational materials science along with condensed-matter physics, where I explore application of machine learning in (i) solving or learning features in correlated systems and (ii) high-throughput ab initio calculations. I learnt ab-initio simulations tools such as VASP/FHI-AIMS to compute energetics of real systems, organize and process the data for ML applications. In material science, I have done a few projects that involve running high-throughput DFT calculations, and processing corresponding data through statistical and ML tools to summarise, refine and predict scientific outcome. While I was at TCD, we also worked on developing a workflow to combine ab-initio and ML tools to build up force fields for simulating large, disordered systems. The ICHEC-Flagship project EuroCC-AF-3 was quite helpful in this direction.
Quantum Computing#
Here at ICHEC, I am part of Quantum Computing initiative, where I explore tools for solving lattice models through Quantum computing Hardware. For this is relatively new, yet exciting area to explore and utilise the many-body knowledge. I am currently involved in developing skills and resources to launch quantum computing tasks in an HPC setting, and looking into possibility of deploying a hybrid classical-quantum workflow for HPC environment.
Note
Here are few bullet points into my activity that span over the above-
Exploring methods for structure property relations of materials with use of ML in High-Throughput ab initio data.
Applying ML in models of many-body physics, such developing ML based lattice density functional theory for models.
Exploring many-body physics models through Quantum computing, both circuit based, and simulation based.
Exploring possibilities of DNNs as generative models for solving many-body problems in correlated systems.