Event
MSE Seminar: Dr. Po-Yen Chen, UMD
Wednesday, May 7, 2025
3:30 p.m.
Room 2110 Chemical and Nuclear Engineering Building
Sherri Tatum
301-405-5240
statum12@umd.edu
“Predictive Design of Sustainable Biobased Packaging via Machine Intelligence for Improved Postharvest Preservation”
Abstract: The widespread use of petrochemical-based plastics in food packaging raises environmental concerns and lacks antimicrobial properties, limiting protection against foodborne microbial contamination. While biobased nanocomposites offer a sustainable alternative, optimizing them remains challenging due to vast formulation possibilities, complex multi-property requirements, and inefficient trial-and-error experimentation. In this talk, I will introduce a data-driven workflow integrating robotic automation, machine learning predictions, density functional theory (DFT) simulations, and life cycle assessment (LCA). An automated pipetting robot formulates 2,420 biobased nanocomposites, and their film quality data train an artificial neural network classifier to define a design space. Within this space, 16 active learning loops iteratively fabricate and characterize 343 biobased nanocomposites with varying formulations, creating a high-quality experimental datasets. Leveraging this dataset and DFT simulations, a prediction model explores ~1 billion formulations, identifying biobased nanocomposites with superior mechanical resilience and tunable transparency. Cluster analysis identifies a Cu2+-incorporated, chitosan-rich film, demonstrating high transparency, toughness, antimicrobial performance, moisture absorption, biocompatibility, and home compostability. Field tests demonstrate that Cu2+-incorporated biobased packaging inhibits microbial growth, significantly extending cucumber shelf life and outperforming plastic wraps. LCA-informed feedback is integrated into the predictive modeling to refine nanocomposite formulations, further reducing environmental impact compared to conventional plastics. A data-sharing platform is developed to integrate predictive modeling with forward prediction and inverse design features. The synergistic integration of robot-assisted experiments, predictive modeling, and simulation tools accelerates the design of sustainable biobased packaging. In this talk, I will also present how my lab applies this machine intelligence-accelerated approach beyond biobased packaging to fields such as stretchable electronics, insulating porous materials, battery electrolyte design, and space agriculture.
Bio: Dr. Po-Yen Chen is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at the University of Maryland (UMD), College Park. He is also an affiliate faculty member of the Maryland Robotics Center (MRC) and the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM). His research lies at the intersection of advanced nanocomposite materials, predictive modeling, and collaborative robotics. Dr. Chen’s lab integrates artificial intelligence (AI)/machine learning (ML) with robot-automated experimentation to accelerate materials discovery. By leveraging advanced active learning frameworks and robotic experimentation, his team constructs high-accuracy predictive models to optimize the development of functional nanocomposites for applications including soft electronics, piezoresistive aerogels, smart soft robots, and sustainable biobased packaging. Through model interpretation and statistical analyses, his research efficiently uncovers formulation–structure–functionality relationships, driving material science innovations through machine intelligence.
Dr. Chen has received multiple prestigious awards throughout his career. In 2019, he was honored with the AME Young Investigator Award and the AIChE SLS Outstanding Young Principal Investigator Award. In 2020, MIT Technology Review named him one of Asia’s Innovators Under 35, and he received the AIChE 35 Under 35 Award. He was elected to the Global Young Academy (GYA) in 2021 and recognized as a Vebleo Fellow. In 2022, he was awarded the John C. Chen Young Professional Leadership Scholarship and invited to the National Academy of Engineering’s U.S. Frontiers of Engineering Symposium as an innovative early-career engineer. His accolades continued with the Young Innovator Award from Nano Research (Springer) in 2023 and the AIChE Shining Star Award in 2024.
