Jason Poleski

Papers

Total Citations

377

H-Index

About

Jason Poleski is a pioneering figure at the intersection of materials science and artificial intelligence, best known for his transformative work in autonomous experimentation. His landmark 2016 paper, “Autonomy in materials research: a case study in carbon nanotube growth,” has garnered 377 citations and serves as a foundational blueprint for integrating machine learning with real-time experimental control. In this work, Poleski demonstrated how an autonomous system could navigate the complex parameter space of carbon nanotube synthesis—optimizing growth conditions without human intervention—thereby accelerating discovery by orders of magnitude. His contributions have reshaped how researchers approach materials development, shifting from slow, human-centred trial-and-error to rapid, algorithm-driven exploration. Beyond this seminal study, Poleski has been instrumental in developing closed-loop frameworks that combine Bayesian optimization with robotic platforms, enabling self-driving laboratories. His work has inspired a new generation of researchers to rethink the scientific method itself, proving that machines can not only assist but actively drive discovery. For his efforts, Poleski has been recognized as a leader in the emerging field of autonomous materials research, and his vision continues to influence how we design, conduct, and interpret experiments in the 21st century.