The discovery and development of new molecules and materials remain constrained by slow, labor-intensive experimental workflows that are poorly matched to the complexity of modern chemical design spaces. This challenge is especially important in colloidal nanomaterials and catalytic systems, where composition, synthesis sequence, reaction environment, and reactor history collectively determine performance.
Milad Abolhasani,
North Carolina State University
To address these limitations, we are developing a data-rich self-driving laboratory ecosystem that integrates reaction and reactor engineering, robotic experimentation, high-throughput synthesis, in-situ multi-modal characterization, and artificial intelligence (AI)-guided decision-making to autonomously explore complex chemical spaces. Rather than simply automating experiments, these systems continuously learn from evolving datasets and adapt their experimental strategies under uncertainty.
In this talk, I will highlight our group’s recent advances in autonomous discovery and optimization of colloidal quantum dots, including metal halide perovskite and II-VI/III-V nanocrystals, with emphasis on route-encoded synthesis, sequence-aware learning, and closed-loop control of optoelectronic properties. I will also discuss autonomous catalysis platforms for accelerated catalyst discovery and process development of specialty and fine chemicals, where multi-robot SDLs enable efficient exploration of high-dimensional catalyst and reaction condition spaces. These advances establish self-driving laboratories as intelligent robotic co-pilots for accelerated molecular and materials discovery, reducing development timelines from years to weeks while generating reproducible, information-rich datasets that reveal transferable chemical knowledge.
Milad Abolhasani is the R.J. Polge Professor and a University Faculty Scholar in the Department of Chemical and Biomolecular Engineering at North Carolina State University, where he also serves as Director of Accelerated Technologies within the Integrative Sciences Initiative. He received his Ph.D. from the University of Toronto in 2014 and subsequently completed postdoctoral training as an NSERC Postdoctoral Fellow in the Department of Chemical Engineering at MIT from 2014 to 2016. At NC State, Dr. Abolhasani leads a multidisciplinary research program focused on data-rich self-driving laboratories for the accelerated discovery, development, and manufacturing of advanced functional materials and molecules. His research integrates reaction and reactor engineering, automation, high-throughput experimentation, and AI to develop autonomous experimentation platforms capable of efficiently navigating complex chemical and materials spaces. Dr. Abolhasani has received numerous awards and honors, including the NSF CAREER Award, the Dreyfus Award for Machine Learning in the Chemical Sciences & Engineering, the AIChE Allan P. Colburn Award, the AIChE Catalysis and Reaction Engineering Early Career Investigator Award, AIChE 35 Under 35 recognition, a Scialog Fellowship, the AIChE NSEF Young Investigator Award, and recognition as part of the I&EC Research 2021 Class of Influential Researchers. He has also been recognized as an Emerging Investigator by Nanoscale, Lab on a Chip, Reaction Chemistry & Engineering, and Digital Discovery.