AI to Enable Better Designs and Better Designing

Date: January 23, 2026; Time: 2:30 PM Location: PWEB 175

Abstract: AI as a means toward better design became an active area of research in the mid-1980s.  Yet today new understandings of how people design, coupled with new approaches to AI (through agents, optimization and deep learning), and more capable computing technologies have enabled better design tools and a better understanding of how AI can help people themselves design.  This talk will look at how AI can accelerate design as a tool, but also how AI can accelerate human teams in the act of designing.  The talk will examine the role of generative design, digital twins of designers, psychological constructs, and the confidence of humans with AI as a guide to new AI-based design methods.

Biographical Sketch: Jonathan Cagan is the David and Susan Coulter Head of Mechanical Engineering and George Tallman and Florence Barrett Ladd Professor at Carnegie Mellon University, with an appointment in Design.  Cagan also served as Interim Dean of the College of Engineering and Special Advisor to the Provost. Cagan co-founded the Integrated Innovation Institute for interdisciplinary design education at CMU, bringing engineering, design and business together to create new products and services.  Cagan’s research focuses on design automation and methods, problem solving, and medical technologies.  His work merges AI, machine learning, and optimization methods with cognitive- and neuro-science problem solving.  A Fellow of the American Society of Mechanical Engineers, Cagan has been awarded the ASME Design Theory and Methodology, Design Automation, Ruth and Joel Spira Outstanding Design Educator, and Computers and Information in Engineering Lifetime Achievement Awards.  He is also a Fellow of the American Association for the Advancement of Science.  A registered Professional Engineer, Cagan received his PhD from UC Berkeley, and has been on the faculty at CMU since 1990.

 Programmable multifunctional materials and structures: Design, realization, and validation

Date: February 27, 2026; Time: 2:30 PM Location: PWEB 175

 

Abstract: Programmable materials and structures hold great potential for various applications, such as robotics, biomedical devices, and civil structures. The rational design, physical realization, and validation of programmed behaviors in these systems play important roles in enabling functional devices. To encode desired mechanical functionality into structures, we propose a multi-material multi-objective topology optimization approach to inverse design composite structures that achieve complex target mechanical responses under large deformations. The multi-material framework simultaneously optimizes both the geometry, material heterogeneity, and architecture to achieve target behaviors and functionalities. A library of diverse designs is created, showcasing a wide range of precisely programmed nonlinear responses, such as multi-bulking and multi-plateau.

In general, the properties of materials and structures typically remain fixed after being constructed. To enable reprogrammable behaviors, we develop a multi-physics topology optimization approach to discover magneto-active and temperature-active materials that achieve tunable buckling and switchable shape morphing, controlled by magnetic fields and temperature fields, respectively. The obtained systems exhibit one response under one stimulus and switch to a distinct response by applying another stimulus.

To bridge the gap between simulation and fabrication, we explore multi-material manufacturing techniques, introduce advanced path generation methods, and develop direct ink writing (DIW) techniques to fabricate a suite of mechanical, magnetic, and thermal metamaterials and metastructures and experimentally validate their programmed behaviors. The excellent agreement among target, simulation, and experiment demonstrates that the proposed optimization-driven framework, when integrated with hybrid manufacturing techniques, has the potential to systematically design, inform, and create innovative multi-functional materials and structures for various engineering applications.

Biographical Sketch: Dr. Xiaojia Shelly Zhang is a David C. Crawford Faculty Scholar and Associate Professor at the Department of Civil and Environmental Engineering and the Department of Mechanical Science and Engineering at the University of Illinois at Urbana Champaign (UIUC). She directs the MISSION (MuIti-functional Structures and Systems desIgn OptimizatioN) Laboratory. Dr. Zhang holds B.S. and M.S. degrees from UIUC and a Ph.D. degree from Georgia Tech. Her research explores multi-physics topology optimization, inverse design, stochastic learning algorithms, and additive manufacturing to develop multi-functional, sustainable, and resilient materials, structures, and robots for applications at different scales. She is the recipient of the National Science Foundation CAREER Award (2021), the ASME Journal of Applied Mechanics Award (2022), the DARPA Young Faculty Award (2022), the AFOSR Young Investigator Award (2023), the Leonardo da Vinci Award from ASCE (2024), the DARPA Director’s Fellowship (2024), UIUC Campus Distinguished Promotion Award (2025), the Thomas J.R. Hughes Young Investigator Award from ASME (2025), the ASME Henry Hess Early Career Publication Award (2025), the Haftka Young Investigator Award from International Society for Structural and Multidisciplinary Optimization (2025). Dr. Zhang serves on the Executive Committee of the International Society of Structural and Multidisciplinary Optimization (ISSMO) and is a Review Editor for the Journal of Structural and Multidisciplinary Optimization and an Associate Editor for the Journal of Applied Mechanics.

Artificial Intelligence-Guided Science of Molten Salts: Chemistry, Structure, and Properties Across the Periodic Table

Date: December 5, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: A central challenge to deploying molten salt nuclear technologies lies in our ability to accurately characterize, predict, and monitor the chemistry of molten salts throughout the fuel cycle. In synthesis, the properties of molten salts can be tailored for specific combination of properties. During operation, fuel salt composition evolves continuously with generation of numerous fission products, which produces significant changes in the thermophysical and thermochemical properties. In reprocessing, impurities must be separated from reusable fuel. Each of these steps requires the study of an enormous array of chemical and thermodynamic conditions. Here, current experimental and computational approaches are not sufficiently accurate and expeditious for assessing these design spaces. As such, it is unlikely that we will achieve the robust chemical understanding required for commercial deployment under conventional research paradigms employed in the study of molten salts. This talk will discuss our latest advances in applying artificial intelligence (AI) to overcome these challenges for studying the chemistry-structure-property relationships in molten salt, which include 1) machine learning (ML)-assisted atomistic simulation for speed and accuracy, 2) chemistry-informed ML for learning the thermal properties of molten salts across the periodic table and generative AI for targeted-property design, and 3) machine learning-enhanced characterization and online monitoring with spectroscopic methods. We will show how state-of-the-art methods have been applied for uncovering structure-property of molten salts with unprecedented speed and resolution and discuss future opportunities for improvement in each of these areas.

Biographical Sketch: Stephen Lam is the Director of Nuclear Engineering, and Assistant Professor of Chemical Engineering at the University of Massachusetts Lowell. His research focuses on combining artificial intelligence and materials simulation to inform experiments for the purpose of understanding chemical structure, reactions and property relationships in advanced energy materials. Stephen obtained a PhD in nuclear engineering in 2020 from the MIT, and BS in Chemical Engineering in 2013 from the University of British Columbia. He was the recipient of the U.S. Department of Energy Early Career Award, and U.S. Nuclear Regulatory Commission’s Distinguished Faculty Advancement Award in 2024. His work includes computational material screening with high-throughput simulation, development of machine learning-based interatomic potentials for predicting properties and understanding microscale phenomena, application of artificial intelligence for unraveling hidden structure-property relationships, and machine learning-assisted spectroscopies for enhancing structural characterization and monitoring techniques. His work has been published in over 30 peer-reviewed articles (including JACS Au, Nature Machine Intelligence, npj Computational Materials, Chemical Science) in areas of machine learning, molten salt chemistry, tritium interactions with materials, carbon materials, and high-temperature ceramics.

Creating Thermal Solutions for Ultrawide Bandgap Electronics

Date: November 7, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: Wide bandgap semiconductors made from GaN and AlGaN alloys have promise for future rf electronics and power switches.  One of the key issues that arises in developing future electronics from these materials is the desire for high-power operation, which will place more demands on managing the heat dissipation from these devices.  This is especially true when using ternary nitride alloys since they possess an intrinsically low thermal conductivity.  This requires careful design of the device architecture and layout to yield effective heat dissipation pathways for wide bandgap semiconductor systems.

In this talk, we will present results on the integration of high thermal conductivity materials with wide bandgap semiconductors as a viable pathway to improve heat dissipation.  We will discuss the important role that interfaces play in enabling the integration of materials such CVD diamond, AlN, and SiC while supporting enhanced heat dissipation. We will present results on the use of new interlayers to reduce the thermal boundary conductance between diamond and nitride semiconductors.  We will also discuss early results on the development of AlN as a semiconductor with promise for future power device applications.  Overall, we will demonstrate the role of modeling in helping to advance the design of thermal solutions for these architectures. Finally, we will discuss the improvements in measurement techniques that allow for the characterization of complex interfaces being developed for advanced nitride rf and power electronics.

Biographical Sketch: Dr. Samuel Graham is the Nariman Farvardin Professor and Dean of Engineering at the University of Maryland.  Prior to joining the University of Maryland, he was a professor and chair of the Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. He holds a joint appointment with the National Renewable Energy Laboratory, serves on the Emerging Technologies Technical Advisory Committee for the U.S. Department of Commerce, the Department of Navy S&T Board, and the Advisory Committee for the Engineering Directorate of NSF.  His research expertise is in the thermal characterization and reliability of wide bandgap semiconductor technologies and the packaging of organic and flexible electronics.

  Solar Thermochemical Hydrogen Production Using Redox Active Materials

Date: October 31, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: Hydrogen is a valuable widely used chemical and an essential component in renewable fuels. Steam-methane reforming is currently used to produce low-cost “grey” hydrogen, that can be turned “blue” by capturing and storing CO2 at extra cost. “Green” hydrogen can be produced via electrolysis at much higher cost. Efforts are underway to advance photosynthesis.  Thermochemical methods, in which high temperature non-stoichiometric reduction of metal oxides is followed by lower temperature oxidation using steam, have the potential to reduce the cost and operate at high-capacity factor. The same technology can also reduce CO2 and produce syngas; an essential feedstock for SAF and efuels. This however requires innovations in redox materials, reactor design and system’s integration. I will introduce the technology and our recent advancements. Ceria is the gold standard because of its stability, but its reduction temperature is high and oxygen carrying capacity is low. Effort to develop alternatives, mostly pervoskites, are underway. Significant reduction-oxidation temperature swing makes it necessary to recover most of the sensible heat. We have designed systems employing multiple reactors that circulate between the two stages to maximize regenerative heat recovery. Generating deep vacuum for reduction, a costly endeavor, can be accomplished by staged oxygen evacuation and novel thermochemical or electrochemical pumping technologies. System level analysis shows that: separation energy should be minimized using, e.g., membrane systems; and waste heat recovery on the exothermic oxidation side should be used to produce electricity to power auxiliary components. To enable continuous operations with optimally sized units, specially designed indirectly heated reactors should operate while communicating with thermal energy storage units. A novel system invented at MIT integrates these ideas and is currently undergoing derisking and validation.

Biographical Sketch: Ahmed F. Ghoniem is the Ronald C. Crane Professor of Mechanical Engineering, Director of the Center for Energy and Propulsion Research and the Reacting Gas Dynamics Laboratory. He received his B.Sc. and M.Sc. degree from Cairo University, and Ph.D. at the University of California, Berkeley. His research covers computational engineering, turbulence and combustion, multiphase flow, clean energy technologies with focus on oxy-combustion for CO2 capture, renewable energy, biofuel and solar fuel production. He supervised more than 120 graduate students and post-doctoral students; published more than 500 articles in leading journals and conferences; and consulted for the aerospace, automotive and energy industry. He is fellow of the ASME, the APS, and the Combustion Institute. He received several awards including the ASME James Harry Potter Award in Thermodynamics, the AIAA Propellant and Combustion Award, the KAUST Investigator Award, the “Committed to Caring Professor” at MIT and the Combustion Institute Bernard Lewis Gold Medal.