Research

Our group has two broad research area with a single goal.

Materials Design

The IMPACT group utilizes a potent fusion of Monte Carlo and Density Functional Theory (DFT) techniques to advance the field of materials design. By employing this synergistic methodology, we are able to conduct an in-depth analysis of the fundamental properties of matter, including the atomic-level electronic structure, energies, and thermodynamics of substances.

We employ the accuracy of Density Functional Theory (DFT) to forecast the behavior of materials under various circumstances, whereas Monte Carlo (MC) simulations provide a statistical perspective on the intricate interaction of variables in complex systems. By harnessing this capability, we explore the domain of disorder effects and unveil labyrinthine pathways to materials possessing customized functionalities.

By adopting this comprehensive strategy, we not only enhance our foundational comprehension of materials but also create opportunities for advancements in various technologies. Our objective is to manipulate the electronic and magnetic characteristics of materials by modifying them at the atomic scale. This customization would specifically concentrate on functionalities such as caloric effects, hydrogen evolution reactions, and qubit materials.

Materials Discovery using ML

By capitalizing on the advantages of both the DFT and the MC method, IMPACT Group accelerates and improves material discovery through the application of machine learning (ML).Despite the provision of precise electronic structure data by DFT, the prohibitive computational expense impedes extensive material investigation. MC simulations are remarkable for examining large configuration spaces, except from the limitation imposed by the configuration space's size.. By incorporating ML into this framework, a material discovery pipeline that is both robust and efficient is established. 

ML models acquire knowledge of the intricate correlations that exist among material structure, composition, and properties via DFT calculations. This knowledge empowers them to forecast the properties of unobserved materials with remarkable precision. Using this method, we can effectively test the most theoretically promising materials found through the extensive MC simulations. This lets us focus our next high-fidelity DFT and MC calculations on those materials.

Hence, by employing this integrated methodology, one can effectively and harmoniously traverse the extensive domain of prospective materials while expediting the identification of materials possessing specific functionalities.

Our exploration spans various material landscapes, including 2D molecules, disordered alloys, Heusler alloys, and MXenes.