Group News

Thermoelectric materials have been of great interest for a number of decades due to their ability to generate power, such as recycling of waste heat. Prasanna Balachandran, assistant professor of materials science and engineering and mechanical and aerospace engineering, is conducting research to deepen theoretical knowledge about polar thermoelectric materials for energy conversion technologies.Balachandran's research focuses on a polycrystalline material known by its chemical formula Ag2GeS3, which is also metastable at ambient conditions.

A research article that was written in collaboration with Prof. Barua's team (VCU) and led by Tim Hartnett is now published in Applied Physics Letters. Congratulations, Tim and the VCU team!ABSTRACT

Prasanna Balachandran receives UVA Research Excellence Award (2020).Excerpt from The UVA Today
“Our dedicated and talented researchers are deeply committed to the mission of this university—advancing knowledge and passing it on to the world and the next generation,” Provost Liz Magill said. “The Research Achievement Awards are a great way to recognize our researchers for making meaningful contributions in their disciplines, supporting their peers and mentees, and having a positive impact on our communities.”

A research article co-authored by Tim Hartnett is now published in AIP Advances. Congratulations, Tim!ABSTRACT

Article published in the Special Issue: Rising Stars in Computational Materials ScienceA generic adaptive machine learning workflow for accelerating the search and discovery of new materials. It has four components: (1) Database, where the problem is defined, (2) machine Learning (using off-the-shelf or Bayesian inference methods), for establishing structure-property relationships. In addition, the formalism must also quantify prediction uncertainties.

Article published: Machine learning accelerates design of single-molecule magnets for magnetocaloric applicationsDesigning new single-molecule magnets (SMMs) for magnetocaloric applications is challenging, because there are millions of possibilities, and only a small fraction of these have been experimentally explored. To improve the search for new magnetocaloric SMMs, Holleis et al. turned to machine learning. The team trained machine learning models to establish a relationship between entropy change and SMM descriptors.