OUR LAB FOCUSES ON...
Accelerating Material Innovations via
Predictive/Generative AI and Autonomous Robotic Platforms
01
Machine Intelligence Accelerated Discovery of Sustainable, Biobased Nanocomposites with Programmable Properties
Our research focuses on integrating predictive machine learning, generative AI tools, and collaborative robotics to accelerate the discovery of sustainable, biobased, functional nanocomposites with programmable properties. We aim to develop all-natural plastic substitutes and biobased, antimicrobial food packaging films to address environmental problems like plastic pollution, food safety and quality, and post-harvest produce contamination.
02
Designing Robust Machine Learning Frameworks for Limited Data Environments
Our research focuses on designing robust machine learning frameworks for limited data environments in materials science and chemical engineering. Given the challenges of working with scarce and expensive experimental data, we aim to develop prediction models that maximize the utility of available datasets. By incorporating techniques such as active learning, transfer learning, data augmentation, and ensemble modeling, we can enhance model accuracy and reliability even with minimal data. This approach has the potential to significantly accelerate materials discovery and process optimization, reducing both the time and cost associated with traditional experimental methods.
03
Mechanically Driven Patterning of Functional Materials for Stretchable Electronics and Soft Robotics
Our research centers on developing a versatile patterning method for creating homogeneous or heterogeneous topographies in various functional materials and their nanocomposites. By leveraging multi-stage mechanical instabilities, our lab aims to generate a library of hierarchical, complex topographies across multiple length scales, tailored for applications in stretchable electronics and soft robotics.
04
Nanoconfined Synthesis of Catalytic Metal Nanocrystals
Our research focuses on using nano-confined effects and various critical synthetic parameters to influence the dimensions, shapes, and distributions of catalytic metal nanocrystals. By uncovering the synthesis–structure–property correlations, we create a library of metal–2D material heterostructured catalysts with superior activity and selectivity.
Contact Us
Affiliations
Department of Chemical and Biomolecular Engineering,
University of Maryland, College Park (UMD)
Maryland Robotics Center (MRC)
Contact
Email: checp@umd.edu
Phone: +1-(669)302-5418
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