ME512 Seminar Series Multimaterial Additive Manufacturing for Shape-Morphing Structures and 4D Printing Monday, May 4, 2026 3:00 PM L211 Tech

ABSTRACT Body 3D printing (additive manufacturing, AM), where materials are deposited in a layer-by-layer manner to form a 3D solid, has seen significant advances in recent decades. Multimaterial 3D printing has attracted significant research efforts in recent years. It offers the advantage of placement of materials with different properties in the 3D space with high resolution, or controllable heterogeneity. In this talk, we present our recent progress in developing multimaterial additive manufacturing methods. In the first approach, we present a new development where we integrate two AM methods, direct-ink-write (DIW) and digital light processing (DLP), into one system. In this system, the DLP can be used to print complex bulk parts while DIW can be used to print functional inks, such as conductive inks and liquid crystal elastomers. In the second approach, we recently developed a grayscale DLP (gDLP) 3D printing method where we use light intensity to control local properties and thus create structures with gradient material properties. We further investigate how to use machine learn to help the inverse design of 4D printing of shape-morphing structures with multimaterial additive manufacturing. BIO Dr. H. Jerry Qi is the Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology and is the site director of NSF IUCRC on Science of Heterogeneous Additive Printing of 3D Materials (SHAP3D). He received his undergraduate and graduate degrees from Tsinghua University and a ScD degree from MIT. After one-year postdoc at MIT, he joined the University of Colorado Boulder in 2004 and moved to Georgia Tech in 2014. Prof. Qi’s research is in the broad field of nonlinear mechanics of polymeric materials and focuses on developing fundamental understandings of multi-field properties of active polymers through experimentation and constitutive modeling, then applying these understandings to application designs. He has been working on a range of active polymers, including shape memory polymers, light-activated polymers, and covalent adaptable network polymers, for their interesting behaviors such as shape memory, light actuation, healing, reprocessing, and recycling. In recent years, he has been working on integrating active materials with 3D printing. He and his collaborators pioneered the 4D printing concept. He is a recipient of NSF CAREER award (2007), Sigma Xi Best Faculty Paper Award (2018), Gerhard Kanig Lecture by the Berlin-Brandenburg Association for Polymer Research (2019), the James R. Rice Medal from Society of Engineering Science (2023), the T. H. H. Pian Award from International Conference on Computational & Experimental Engineering and Sciences (2024), and the ASME Warner T. Koiter Medal (2024). He was listed as one of the highly cited researchers by Clarivate in 2024 and 2025

ABSTRACT

GScientific Machine Learning (SciML) integrates data-driven inference with physical modeling to solve complex problems in science and engineering. However, the design of SciML architectures, loss formulations, and training strategies remains an expert-driven research process, requiring extensive experimentation and problem-specific insights. We introduce AgenticSciML, a collaborative multi-agent system in which over 10 specialized AI agents collaborate to propose, critique, and refine SciML solutions through structured reasoning and iterative evolution. The framework integrates structured debate, retrieval-augmented method memory, and ensemble-guided evolutionary search, enabling the agents to generate and assess new hypotheses about architectures and optimization procedures. Across physics-informed learning and operator learning tasks, the framework discovers solution methods that outperform single-agent and human-designed baselines by up to four orders of magnitude in error reduction. The agents produce novel strategies—including adaptive mixture-of-expert architectures, decomposition-based PINNs, and physics-informed operator learning models—that do not appear explicitly in the curated knowledge base. These results show that collaborative reasoning among AI agents can yield emergent methodological innovation, suggesting a path toward scalable, transparent, and autonomous discovery in scientific computing.

BIO
George Karniadakis is from Crete. He is an elected member of the National Academy of Engineering, National Academy of Arts and Sciences, and a Vannevar Bush Faculty Fellow. He received his S.M. and Ph.D. from the Massachusetts Institute of Technology (1984/87). He was appointed Lecturer in the Department of Mechanical Engineering at MIT and subsequently joined the Center for Turbulence Research at Stanford/NASA Ames. He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continued to be a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018–), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010–), Fellow of the American Physical Society (APS, 2004–), Fellow of the American Society of Mechanical Engineers (ASME, 2003–), and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006–). He received the William Benter Award (2026), the SES G.I. Taylor Medal (2014), the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt Award (2017), the SIAM Ralf E. Kleinman Award (2015), the J. Tinsley Oden Medal (2013), and the CFD Award (2007) by the US Association for Computational Mechanics. His h-index is 160 (highest in Applied Mathematics) and he has been cited over 156,000 times.

BIO

Treasured member of the Northwestern faculty from 1977 until his death in 2014, Ted Belytschko was a central figure in the McCormick community and an internationally renowned researcher who made major contributions to the field of computational structural mechanics. One of the most cited researchers in engineering science, Belytschko developed explicit finite element methods that are widely used in crashworthiness analysis and virtual prototyping in the auto industry. He received numerous honors, including membership in the U.S. National Academy of Engineering, U.S. National Academy of Sciences, and the American Academy of Arts and Sciences. He was a founding director of the U.S. Association for Computational Mechanics, and in 2012, the association named a medal in his honor. The ASME Applied Mechanics Award was renamed the ASME Ted Belytschko Applied Mechanics Division Award in November 2007. Belytschko also served as editor-in-chief of the International Journal for Numerical Methods in Engineering, and he was co-author of the books “Nonlinear Finite Elements for Continua and Structures” and “A First Course in Finite Elements.”

ABOUT TED BELYTSCHKO
Treasured member of the Northwestern faculty from 1977 until his death in 2014, Ted Belytschko was a central figure in the McCormick community and an internationally renowned researcher who made major contributions to the field of computational structural mechanics. One of the most cited researchers in engineering science, Belytschko developed explicit finite element methods that are widely used in crashworthiness analysis and virtual prototyping in the auto industry. He received numerous honors, including membership in the U.S. National Academy of Engineering, U.S. National Academy of Sciences, and the American Academy of Arts and Sciences. He was a founding director of the U.S. Association for Computational Mechanics, and in 2012, the association named a medal in his honor. The ASME Applied Mechanics Award was renamed the ASME Ted Belytschko Applied Mechanics Division Award in November 2007. Belytschko also served as editor-in-chief of the International Journal for Numerical Methods in Engineering, and he was co-author of the books “Nonlinear Finite Elements for Continua and Structures” and “A First Course in Finite Elements.”

Co-sponsored by the Departments of Mechanical Engineering and Civil & Environmental Engineering

Mechanics of architected materials across length and time scales

Abstract

Architected materials (or mechanical metamaterials) across length scales—from nanometers to centimeters— have enabled previously unachievable mechanical properties through a variety of 3D material morphologies. Significant advances in our understanding of these materials have thus pointed to structure-property relations that lead to unique macroscopic mechanical properties. Despite this progress, several hurdles have precluded widespread application of these materials to solve engineering challenges. First, clear routes to scalably design and fabricate these architected materials have remained elusive; with most designs reported to date targeting high stiffness but low deformability. Second, since most of the studies to date have characterized architected materials under quasi-static deformation, their dynamic-property regime remains to be fully characterized and understood—essential to a variety of envisioned applications. In this talk, we present efforts towards addressing these long-standing challenges, specifically by proposing routes for designing architected materials with extreme compliance and by presenting two types of high-throughput characterization methods that enable exploration of architected materials under dynamic conditions. We discuss efforts implementing and understanding compliant architected materials by proposing metamaterial design paradigms inspired by polymer-network architectures. We also present efforts performing non-contact characterization of materials through laser-induced vibrational signatures, providing a new route to uncover dynamic elastic responses of materials and unparalleled throughput rates. Lastly, we discuss efforts characterizing architected materials under extreme dynamic conditions through use of microparticle impact experiments at the microscale, shedding light on energy dissipation mechanisms that emerge from the use of 3D microstructure.

Bio

Carlos Portela is the Robert N. Noyce Career Development Associate Professor in Mechanical Engineering at MIT. Portela received his Ph.D. and M.S. in mechanical engineering from the California Institute of Technology, where he was given the Centennial Award for the best thesis in Mechanical and Civil Engineering. His research lies at the intersection of mechanics, nano-to-macro fabrication, and materials science with the objective of designing and testing novel materials—with features spanning from nanometers to centimeters—that yield unprecedented mechanical and acoustic properties. Portela is the recipient of the 2026 ONR YIP Award, a 2024 ARO Early Career Program Award, was recognized as an MIT TR Innovator Under 35 in 2022, and was a recipient of the 2022 NSF CAREER Award. His teaching efforts have been recognized by MIT’s 2025 Jr. Bose Award and the 2023 Spira Award for Excellence in Teaching.