Advances in materials are an important contributor to our technological progress, and yet the process of materials discovery and development itself is slow. Our current research process is human-centred, where human researchers design, conduct, analyse and interpret experiments, and then decide what to do next. We have built an Autonomous Research System (ARES)-an autonomous research robot capable of first-of-its-kind closed-loop iterative materials experimentation. ARES exploits advances in autonomous robotics, artificial intelligence, data sciences, and high-throughput and in situ techniques, and is able to design, execute and analyse its own experiments orders of magnitude faster than current research methods. We applied ARES to study the synthesis of singlewalled carbon nanotubes, and show that it successfully learned to grow them at targeted growth rates. ARES has broad implications for the future roles of humans and autonomous research robots, and for human-machine partnering. We believe autonomous research robots like ARES constitute a disruptive advance in our ability to understand and develop complex materials at an unprecedented rate.

Applications of carbon nanotubes continue to advance, with substantial progress in nanotube electronics, conductive wires, and transparent conductors to name a few. However, wider application remains impeded by a lack of control over production of nanotubes with the desired purity, perfection, chirality, and number of walls. This is partly due to the fact that growth experiments are time-consuming, taking about 1 day per run, thus making it challenging to adequately explore the many parameters involved in growth. We endeavored to speed up the research process by automating CVD growth experimentation. The adaptive rapid experimentation and in situ spectroscopy CVD system described in this contribution conducts over 100 experiments in a single day, with automated control and in situ Raman characterization. Linear regression modeling was used to map regions of selectivity toward single-wall and multiwall carbon nanotube growth in the complex parameter space of the water-assisted CVD synthesis. This development of the automated rapid serial experimentation is a significant progress toward an autonomous closed-loop learning system: a Robot Scientist.

The physical state of the catalyst and its impact on the growth of single-walled carbon nanotubes (SWNTs) is the subject of a long-standing debate. We addressed it here using in situ Raman spectroscopy to measure Fe and Ni catalyst lifetimes during the growth of individual SWNTs across a wide range of temperatures (500-1400 °C). The temperature dependence of the Fe catalyst lifetimes underwent a sharp increase around 1100 °C due to a solid-to-liquid phase transition. By comparing experimental results with the metal-carbon phase diagrams, we prove that SWNTs can grow from solid and liquid phase-catalysts, depending on the temperature.