We present an autonomous nanomaterials discovery robot that searches for diversified nanostructures with real-time feedback, theory, and machine learning in a vast synthetic space. The robotic platform conducts open-ended exploration of the seed-mediated multistep synthesis of gold nanoparticles (AuNPs) via in-line UV-Vis characterisation and discovers five classes of nanoparticles by only needing to perform ca. one thousand experiments from a potential space of ca. &gt;10<sup>23</sup> experiments. The platform then performs the optimisation of nanostructures with desired optical properties by combing experiments and scattering simulations. With the efficient fine-tuning of optical signals, the optimisation achieves a significantly higher shape yield (up to 95%) and discovers multiple nanostructures. To enable the standardisation of nanomaterials synthesis, digital signatures of nanoparticles derived from synthesis codes using our Chemical Description Language, χDL and the expected physical properties are conceptualized and implemented. This unified architecture towards exploration, optimisation and digital synthesis with unique signatures opens new horizons towards complete and efficient digitisation of nanomaterials discovery.

Autonomous intelligent exploration, discovery, and optimisation of nanomaterials

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ACS Spring 2023

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