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 can reliably optimise the conditions to produce gold-nanoparticles over many cycles, discovering new synthetic conditions for known nanoparticle shapes 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 independent cycles of evolution we show our autonomous system can produce spherical nanoparticles, rods, and finally octahedral nanoparticles by using our optimized rods as seeds.

We present an autonomous chemical synthesis robot for the exploration, discovery, and optimization of nanostructures driven by real-time spectroscopic feedback, theory, and machine learning algorithms that control the reaction conditions and allow the selective templating of reactions. This approach allows the transfer of materials as seeds between cycles of exploration, opening the search space like gene transfer in biology. The open-ended exploration of the seed-mediated multistep synthesis of gold nanoparticles (AuNPs) via in-line ultraviolet-visible characterization led to the discovery of five categories of nanoparticles by only performing ca. 1000 experiments in three hierarchically linked chemical spaces. The platform optimized nanostructures with desired optical properties by combining experiments and extinction spectrum simulations to achieve a yield of up to 95%. The synthetic procedure is outputted in a universal format using the chemical description language (χDL) with analytical data to produce a unique digital signature to enable the reproducibility of the synthesis.

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry.

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Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone.