September 8
@
4:00 PM
–
5:00 PM
Milad Abolhasani
R.J. Polge Professor
North Carolina State University
Raleigh, NC
Data-Rich Self-Driving Laboratories for Accelerated Materials and Molecular Discovery
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. To address these limitations, we are developing a data-rich self-driving laboratory (SDL) ecosystem that integrates reaction and reactor engineering, robotic experimentation, high-throughput synthesis, in-situ multi-modal characterization, and artificial intelligence-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 SDLs 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.