OUR LAB FOCUSES ON...
Accelerating Materials Synthesis and Discovery via
Predictive/Generative AI and Autonomous Robotic Platforms
01
Machine Intelligence Accelerated Discovery of Sustainable Biopolymers with Programmable Properties
Our research leverages machine learning (ML)-driven predictive and generative modeling alongside robotic automation to accelerate the synthesis and discovery of sustainable biopolymers. By integrating ML with high-throughput experimentation, we design sustainable biopolymers with programmable properties intended to replace conventional plastics. Our mission is to engineer high-performance, biodegradable alternatives and antimicrobial packaging that tackle global challenges in plastic waste, food safety, and postharvest preservation.
02
Explainable and Robust Machine Learning for Data-Sparse Materials Discovery
We are overcoming the "small data" challenge in materials discovery by building explainable and resilient machine learning (ML) architectures. Our research leverages data augmentation, ensemble modeling, and active/transfer learning to navigate regimes where experimental data is scarce or costly to obtain. By transforming how we analyze limited datasets, we provide a scalable alternative to conventional experimentation—bypassing traditional trial-and-error bottlenecks and drastically reducing the time and resources required to optimize complex formulations and manufacture next-generation functional materials.
03
Multiscale Topographies for High-Strain Functional Materials for Stretchable Electronics and Soft Robotics
We specialize in leveraging multi-stage mechanical instabilities to engineer diverse surface topographies on functional materials and nanocomposite films. Our lab has developed a comprehensive library of hierarchical, multiscale surface architectures that are tailored for high mechanical adaptability. These "mechanically intelligent" structures serve as the foundation for stretchable electronics, soft robotics, and deformable batteries, providing the strain-tolerance and functional performance required for next-generation wearable technologies.
04
Nano-confined Synthesis of Catalytic Metal Nanocrystals
We investigate how spatial confinement and critical synthetic parameters govern the morphology and distribution of catalytic metal nanocrystals. By elucidating the fundamental synthesis–structure–property relationships, we engineer a diverse library of metal–2D material heterostructures. These precisely architected catalysts are designed for enhanced activity and selectivity, driving high-efficiency performance in targeted chemical transformations.
Contact Us
Affiliations
University of Maryland, College Park (UMD)
Department of Chemical and Biomolecular Engineering
Department of Electrical and Computer Engineering
Maryland Robotics Center (MRC)
Artificial Intelligence Interdisciplinary Institute at Maryland (AIM)
Contact
Addresses
Campus Office: Room 1223C, 4418 Stadium Drive, College Park, MD 20742-2111
Research Lab: Room 1216, J. M. Patterson Building, College Park, MD 20742-2111
Zoom Office: Link