Autonomy in materials research: a case study in carbon nanotube growth

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.

Effect of the Laser Heating of Nanotube Nuclei on the Nanotube Type Population

Many potential applications of carbon nanotubes are expected to benet from the availability of single-walled carbon nanotube materials enriched in metallic species,and specifically armchair nanotubes.The present work focuses on the modification of the pulsed laser vaporization(PLV)technique to selectively produce certain carbon nanotube structures.Nanotube nuclei were“warmed-up”with an additional laser pulse,timed to coincide approximately with the nucleation event.The effect of the second laser on the carbon vapor temperature was studied by emission spectroscopy.Nanotube type populations with and without warm-up were compared by means of absorption,photoluminescence,and Raman spectroscopy.It was found that the warm-up of nanotube nuclei with a laser pulse has a noticeable,albeit small,effect on the nanotube population.The intensity of spectral features associated with(9,7)nanotube and its large chiral angle neighbors increased,while small chiral angle nanotubes decreased,with exception of the(15,0)tube.This experiment demonstrates that nanotube population during PLV synthesis can be manipulated in a controlled fashion.

Advanced machine learning decision policies for diameter control of carbon nanotubes

The diameters of single-walled carbon nanotubes(SWCNTs)are directly related to their electronic properties,making diameter control highly desirable for a number of applications.Here we utilized a machine learning planner based on the Expected Improvement decision policy that mapped regions where growth was feasible vs.not feasible and further optimized synthesis conditions to selectively grow SWCNTs within a narrow diameter range.We maximized two ranges corresponding to Raman radial breathing mode frequencies around 265 and 225 cm^(−1)(SWCNT diameters around 0.92 and 1.06 nm,respectively),and our planner found optimal synthesis conditions within a hundred experiments.Extensive post-growth characterization showed high selectivity in the optimized growth experiments compared to the unoptimized growth experiments.Remarkably,our planner revealed significantly different synthesis conditions for maximizing the two diameter ranges in spite of their relative closeness.Our study shows the promise for machine learning-driven diameter optimization and paves the way towards chirality-controlled SWCNT growth.