Oak Ridge National Laboratory's Ayana Ghosh, a research scientist at the Department of Energy's facility, has clinched the 2024 Early Discovery Award from the American Ceramic Society. This recognition singles out an early career member for notable contributions to the fundamental science of glass and ceramics. Ghosh, who brought her expertise in theory and simulations across several ORNL projects, has been especially influential through her work with artificial intelligence initiatives and INTERSECT.
Ghosh aims to further integrate causal machine learning into the study of physics and materials science, a pursuit that stirs her professional curiosity. "What really fascinates me is looking into these types of methods solving fundamental physics or material science problems because I have not seen a lot of papers exploring this area," expressed Ghosh, according to the Oak Ridge National Laboratory. She is set to explore the further application of causal machine learning, which deals with cause-effect relationships and prediction of outcomes, across various material science challenges.
Having joined ORNL in 2020 as a postdoctoral researcher, Ghosh quickly moved up the ranks to become a full-time research scientist in 2023. Her academic journey through the University of Connecticut, leading to her master's and doctoral degrees, and her experiences at Los Alamos National Laboratory have contributed to her stellar rise in the field of computational chemistry and nanomaterials.
Ghosh's team is particularly excited about upcoming projects, including simulations on the Summit supercomputer. "There are some large-scale simulations we have been running, so it will be great to see if there are utilities for these types of methods," she told Oak Ridge National Laboratory's newsroom. The objective is to scale up and conduct causal analysis to understand the underlying physical and causal mechanisms in larger material systems.
The American Ceramic Society's nod to Ghosh is a testament to her impactful research in an arena where competition is stiff, and her recognition brings to light the groundbreaking potential of machine learning in traditional scientific disciplines. Meanwhile, UT-Battelle continues to manage ORNL on behalf of the Department of Energy's Office of Science, the primary patron of basic research in the physical sciences across the nation, aiming to tackle some of the important challenges of our era.
