Computational Condensed Matter and Materials Physics
Our group excels in interdisciplinary research at the nexus of materials science, quantum chemistry, computational physics, and theoretical condensed matter. We are dedicated to harnessing data-driven methodologies and machine learning (ML) innovations to understand and predict the electronic, structural, optical, mechanical, transport, and magnetic properties of advanced materials. Our research encompasses a diverse array of materials, from novel 2D structures and bulk materials to nanostructures and composites.
In our pursuit of understanding and predicting material properties, we employ a comprehensive suite of computational techniques, anchored by density functional theory (DFT) and first-principles-based many-body approaches. Our work is further enhanced by the integration of advanced ML algorithms and data analytics, enabling us to uncover deeper insights and accelerate the discovery of materials with tailored properties. Through these advanced computational methodologies, we strive to push the boundaries of materials science and engineering, making pivotal contributions to the development of next-generation materials for a wide array of applications.
Motivated students and postdocs that are interested in developing new tools for computational condensed matter and materials physics are encouraged to contact us.
Postdoc Opportunity!
We have an opening in our group for a two year postdoc starting in fall 2024. Please apply here: https://academicjobsonline.org/ajo/jobs/27160
If you have questions please contact us.
