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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.
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The study of nanoparticles has increased vastly due to their unique properties, leading to new developments in many different areas such as surface enhanced Raman scattering (SERS)1,2, microscopy3, drug delivery agents4,5, cancer treatment5,6, carriers for biomolecules7, etc8,9,10. For this reason, several synthetic protocols have emerged such as electrochemical11,12, photochemical13, template14,15, Turkevich16,17, or seed-mediated growth18,19, to form different shapes of nanoparticles e.g. spheres20, rods21,22,23, cubes24,25, etc. with a host of different properties. Despite the fact that so many synthetic methods have been developed, these have proven difficult to control and produce large amounts of by-products, as well as having problems with reproducibility that have made the synthesis of gold nanoparticles quite challenging10. This means the ability to precisely control the shape of the nanoparticle, and therefore its physical properties, and application, can be challenging for the discovery and for the process of reproducing the protocol. Indeed, the difficulty in the reproduction of known protocols is a major bottleneck preventing the extended development and use of such materials.
To address these fundamental issues, we hypothesised that the algorithm-driven discovery and digital control of synthesis using a robotic system could revolutionise the design and control of complex faceted nanoparticles. Indeed algorithms have recently been used in self-optimising chemical reactions26, exploring catalysis27 and also the nucleation of nanocrystals in microfluidic devices28. This is because the robotic system could allow the high-fidelity reproduction of the methods used to discover the nanoparticles, and this code could be replayed to generate the clusters again minimising errors. In addition, we wanted to use a genetic algorithm approach that not only uses an electronic genome, but also explores the idea that it is possible to evolve physical objects. These objects are not only improved by evolution towards a target, but then could then be used as physical seeds to help direct to new targets. This means the evolutionary trajectory is also imprinted into the physical object, rather than just being weakly associated in an electronic genome. The idea of embodied evolution29 is mostly confined to robotics, but also has been explored with some materials30.
a Chemical space 1, containing reagents for the synthesis of spheres, was explored using the platform until spectral target 1 (spheres) was reached/optimised. b Known literature seeds10 used in chemical space 2 with known reagents for the synthesis of rods until spectral target 2 (rods) was reached/optimised. c Target 2 optimised rods used to as seeds to achieve target 3 spectra for unknown nanoparticle shape outcome.
The first stage of our work began with acquiring an expected UV signal for known gold nanospheres from a commercially available source (Sigma-Aldrich) in order to establish the spectral target 1 for the automated system. Next, we synthesised gold nanorods following a synthesis described in the literature10 in order to obtain spectral target 2 for the platform. The platform begins with a set of reagents in order to synthesise spheres aiming for the designated spectroscopic target using the genetic algorithm. Once the platform has reached the desired target, the optimised particles are analysed. The platform was then given the next set of additional reagents required to synthesise gold nanorods along with the new objective function targeted in the UV-Vis. The ratios of those additional reagents are estimated and optimised by means of the genetic algorithm. Once the system has obtained optimal rods, these particles are used as the seeds in order to synthesise other types of nanomaterials with an objective we set, rather than values based on the literature, see Fig. 2.
(proceeding clockwise from top left position) The platform itself (see SI for build details). Each new series of reaction generations aims for a specified spectral target, beginning with a random exploration of the chemical space. Volumes of stock reagents are initially selected at random, dispensed by the platform and analysed by in-line UV-Vis spectroscopy. The resultant spectra are assigned a fitness value and evaluated via our genetic algorithm. The algorithm mutates the experimental parameters and crosses them over attributes of the highest fitness samples to generate new experimental parameters for the next generation. The cycle repeats until the target was reacted, each 15-reaction generation of a given series proceeding towards the predefined spectra.
Algorithm. In order for the robot to facilitate autonomous synthesis and decision making, an heuristic artificial intelligence search method was employed based upon a genetic algorithm (GA)31. GAs are often used for finding optimised solutions to search problems inspired, and loosely based, on the theory of natural selection however normally physical material is not passed between optimisation runs. Genetic algorithms are excellent for searching through large and complex data sets and are considered capable of finding reasonable solutions when a large number of variables must be explored.
Although the synthesis of AuNPs of different size and shape has been studied before, our work presents a new methodology to further and advance this field by using the unbiased nature of algorithmically driven synthesis in a closed loop robot platform. The platform presented in this paper has been able to synthesise complex nanomaterials starting from simple, raw chemicals by a process of hierarchical evolution. Our system has demonstrated for the first time, seed mediated nanoparticle synthesis assisted by an evolutionary algorithm in a controlled and reproducible manner. This automated, closed loop approach allows us not only to create known architectures reliably but also could be used as a tool to discover complex nano-constructs using desired spectroscopic responses. Lower tier nanoparticles were fed into the system in order to obtain more complex structures. This methodology, whilst offering the benefits of automation; speed, safety, reproducibility, etc. provides the chemist with a tool for developing new synthetic methods and the potential for new discoveries. These discoveries could lead to a better understanding of how nanoparticles are formed and to develop new application areas by searching for a given property, as well as ensuring that complex faceted nanoparticles can be reproduced easily using a digital code in an automatic platform32,33.
L.C. devised the concept and the initial algorithm and platform. The platform was built by D.S., G.K. and J.G.; D.S. and G.K. helped collect data. S.M. helped in the early stages of the project. J.G. and A.S. helped L.C. coordinate the team. All the co-authors helped write the manuscript with L.C.
The fabrication of nanomaterials fromthe top-down gives precise structures but it is costly, whereas bottom-up assemblymethods are found by trial and error. Nature evolves materials discovery byrefining and transmitting the blueprints using DNA mutations autonomously. Geneticallyinspired optimisation has been used in a range of applications, from catalysisto light emitting materials, but these are not autonomous, and do not usephysical mutations. Here we present an autonomously driven materials-evolution roboticplatform that allows us to reliably discover the conditions to producegold-nanoparticles that can run for many cycles, discovering entirely newsystems 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 frompreceding generations to engineer more sophisticated architectures. Over threecycles of evolution we show the seeds from each generation can producespherical nanoparticles, rods, and highly anisotropic arrow-facetednanoparticles.
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MIT researchers have pioneered a multirobot simultaneous localization and mapping (SLAM) collaborative system that they call Kimera-Multi, whose results are pictured above a photo of eight robots that carried out the mapping task. 658cc48d5c
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