A Nanomaterials Discovery Robot for the Darwinian Evolution of Shape Programmable Gold Nanoparticles

13 June 2019, Version 1
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.

Abstract

The fabrication of nanomaterials from the top-down gives precise structures but it is costly, whereas bottom-up assembly methods are found by trial and error. Nature evolves materials discovery by refining and transmitting the blueprints using DNA mutations autonomously. Genetically inspired optimisation has been used in a range of applications, from catalysis to light emitting materials, but these are not autonomous, and do not use physical mutations. Here we present an autonomously driven materials-evolution robotic platform that allows us to reliably discover the conditions to produce gold-nanoparticles that can run for many cycles, discovering entirely new systems using the opto-electronic properties as a driver. Not only can we reliably discover a method, encoded digitally to synthesise these materials, we can seed in materials from preceding generations to engineer more sophisticated architectures. Over three cycles of evolution we show the seeds from each generation can produce spherical nanoparticles, rods, and highly anisotropic arrow-faceted nanoparticles.

Keywords

materials discovery robot
closed loop
nanomaterials
artificial intelligence

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