Data-Driven Computational Synthesis of 2D van der Waals Materials with Organic Molecules

The dawn of machine learning and materials informatics has ushered in a new era of data-driven materials design and discovery. As we delve into the expansive computational space of materials properties, these data-centric methodologies offer a streamlined approach, efficiently pinpointing regions of interest and predicting promising candidates. This not only accelerates the discovery process but also reduces computational costs significantly. A prime example of this advancement is in the exploration of 2D van der Waals materials. When integrated with organic molecules, these materials showcase promising and novel properties. Through a data-driven lens, we can expedite the identification and optimization of these hybrid systems, moving us closer to real-world applications. This talk will shed light on how machine learning and materials informatics are paving the way for innovative materials design, with a special emphasis on the interplay between 2D van der Waals materials and organic molecules.
Chinedu Ekuma Biography
Prof. Ekuma earned his Ph.D. in Physics from Louisiana State University under Prof. Mark Jarrell. After roles at the U.S. Army Research Laboratory and the U.S. Naval Research Laboratory, he joined the Lehigh University faculty in 2019. His research focuses on strongly correlated materials, exploring the dynamics between strong interactions and nanoscale defects. His methodologies include materials informatics, machine learning, ab initio density functional theory, and many-body approaches, which he uses to investigate and design emerging material properties.