Event
CHBE Seminar: Dr. Po-Yen Chen, UMD
Friday, September 12, 2025
11:00 a.m.
Room 2108 Chemical and Nuclear Engineering Building
Patricia Lorenzana
301-405-1935
plorenza@umd.edu
"From Limited Data to Scalable Discovery: A Robotics and Machine Learning Integrated Workflow for Accelerated Materials Innovation”
Abstract: The development of next-generation functional materials is often constrained by small, high-variance datasets and the inefficiency of labor-intensive, trial-and-error experimentation. To overcome these barriers, we present a robotics–machine learning integrated workflow that transforms limited data into scalable discovery. Automated robotic platforms enable high-throughput and reproducible experimentation, rapidly generating diverse datasets across high-dimensional design spaces. Machine learning strategies, incorporating active learning and data augmentation, construct ensemble-based predictive models that deliver accurate and robust performance even under data scarcity. By coupling these models with interpretability tools, the robotics–machine learning integrated workflow expands accessible property boundaries and accelerates the optimization of functional materials with tunable properties.
As a case study, we demonstrate this approach in the discovery of sustainable biobased nanocomposite packaging films. An automated pipetting robot formulated 2,420 nanocomposites, whose film quality data trained an artificial neural network classifier to define a design space. Within this space, 16 active learning loops iteratively fabricated and characterized 343 nanocomposites, producing a high-quality dataset. Leveraging this dataset alongside density functional theory simulations, a prediction model explored ~1 billion formulations, identifying candidates with superior mechanical resilience and tunable transparency. Among them, Cu²⁺-incorporated nanocomposite films exhibited moisture absorption, oxygen impermeability, and antimicrobial performance, outperforming conventional plastics and extending the shelf life of postharvest produce. Life cycle assessment–informed feedback further refined the formulations, and a data-sharing platform was established to promote adoption. This case study highlights the power of the robotics–machine learning workflow in accelerating materials discovery from limited, high-variance datasets, offering a scalable pathway for the predictive design of advanced and sustainable materials.
Bio: Dr. Po-Yen Chen is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at the University of Maryland, College Park (UMD). He holds affiliated appointments with the Maryland Robotics Center (MRC) and the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM). Dr. Chen earned his B.S. in Chemical Engineering from National Taiwan University (NTU) and his Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology (MIT). After completing his doctoral studies, he was awarded the Hibbitt Early Career Fellowship and conducted independent research as a fellow at Brown University for two years. Prior to joining UMD, he served as an Assistant Professor in the Department of Chemical and Biomolecular Engineering at the National University of Singapore (NUS) for two and a half years.
Dr. Chen’s research integrates machine learning (ML)-enabled predictive and generative modeling with robotics-automated experimentation and advanced simulation tools to accelerate the discovery and optimization of high-performance, sustainable materials. By harnessing ML-driven predictions, his work addresses longstanding challenges in multi-component formulation and multi-property optimization—overcoming the limitations of traditional trial-and-error and one-factor-at-a-time approaches. This robotics- and ML-integrated workflow significantly accelerates the innovation of functional materials with programmable properties. It tackles urgent global challenges such as plastic pollution, resource depletion, and the demand for sustainable, high-performance alternatives. The AI- and ML-discovered materials developed through his research have broad potential applications, including soft electronics, conductive aerogels, smart soft robotics, and next-generation sustainable technologies. Furthermore, his work leverages model interpretation techniques and simulation-based insights to uncover complex composition–structure–functionality relationships, enabling the rational design of materials with precisely tailored properties.
