Self-driving laboratories can compress years of materials discovery into weeks. Instead of relying on trial-and-error, they use algorithms to learn from each experiment and decide what to try next. When experiments are expensive and budgets are limited, choosing the next experiment well can have a large impact on the quality of the material ultimately discovered. This event will present results from a recent benchmark of 46 data-driven optimization solvers on 502 problems, ranging from one to 300 dimensions and spanning a wide variety of mathematical behavior.

Closed-loop discovery of functional molecules represents a new paradigm to new materials discovery. However, so far, AI-driven closed-loop discovery approaches have rarely yielded new chemical insight. I am leading a team to address this challenge under the Molecule Maker Lab Institute, one of the first AI Institutes in US. We connect autonomous synthesis, autonomous device printing and testing, and machine learning in a closed loop to drive molecular design of light harvesting small molecules towards high photostability.

This presentation will provide an overview of ongoing industrial membrane research for natural gas processing. Current needs in membrane material science will be discussed. Because membrane modules operate within an ecosystem of other system components, the impact of membrane material properties will be contextualized among cost drivers in natural gas processing. Finally, the role of modeling will be explored, including prediction of module performance from laboratory-scale transport measurements, which can then be used to direct material discovery efforts to shorten time to commercialization.

Effectively monitoring and managing pain remain major challenges in the clinic and constitute active areas of research today. There is a critical need to both identify new non-opioid drug targets for chronic pain management and provide more objective ways to measure pain. To improve the treatment and measurement of pain, we describe here the development of prototypical pain biosensors.

Electrostatic interactions can be used in the self-assembly of a wide range of responsive, bioinspired soft materials ranging from dehydrated thin films, fibers, and bulk solids to dense, polymer-rich liquid complex coacervates, as well as more complex hierarchical structures such as micelles and hydrogels. This responsiveness can include swelling and dissolution or solidification, which can be harnessed to facilitate encapsulation and the subsequent fabrication of functional materials. We draw inspiration from biomolecular condensates, which utilize liquid-liquid phase separation to create transient compartments in cells.

The convergence of AI and human induced pluripotent stem cell (hiPSC) platforms is redefining in vitro biological models. Advances in hiPSC-derived three-dimensional cardiac organoids, engineered heart tissues, and heart-on-chip systems have improved physiological relevance but generate high-dimensional, multimodal datasets that exceed the capacity of traditional analytical approaches. I will discuss how emerging computational approaches, including machine learning, deep learning, and generative AI, can unlock the full potential of engineered cardiac models.

The innate immune response must coordinate diverse cell populations to eliminate pathogens, heal wounds, and maintain tissue homeostasis. This coordination depends on dynamic communication between immune cells and their surrounding tissue environment, yet even subtle disruptions in these interactions can drive chronic inflammation and contribute to diseases including cancer, cardiovascular disease, fibrosis, and autoimmunity. As immunotherapies continue to transform the treatment of disease, a central challenge remains: understanding how innate immune cells integrate complex environmental cues to execute precise, context-dependent functions.