Networking chemical robots for reaction multitasking

The development of the internet of things has led to an explosion in the number of networked devices capable of control and computing. However, whilst common place in remote sensing, these approaches have not impacted chemistry due to difficulty in developing systems flexible enough for experimental data collection. Herein we present a simple and affordable (<$500) chemistry capable robot built with a standard set of hardware and software protocols that can be networked to coordinate many chemical experiments in real time. We demonstrate how multiple processes can be done with two internet connected robots collaboratively, exploring a set of azo-coupling reactions in a fraction of time needed for a single robot, as well as encoding and decoding information into a network of oscillating reactions. The system can also be used to assess the reproducibility of chemical reactions and discover new reaction outcomes using game playing to explore a chemical space

A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticles

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

A modular programmable inorganic cluster discovery robot for the discovery and synthesis of polyoxometalates

The exploration of complex multicomponent chemical reactions leading to new clusters, where discovery requires both molecular self-assembly and crystallization, is a major challenge. This is because the systematic approach required for an experimental search is limited when the number of parameters in a chemical space becomes too large, restricting both exploration and reproducibility. Herein, we present a synthetic strategy to systematically search a very large set of potential reactions, using an inexpensive, high-throughput platform that is modular in terms of both hardware and software and is capable of running multiple reactions with in-line analysis, for the automation of inorganic and materials chemistry. The platform has been used to explore several inorganic chemical spaces to discover new and reproduce known tungsten-based, mixed transition-metal polyoxometalate clusters, giving a digital code that allows the easy repeat synthesis of the clusters. Among the many species identified in this work, the most significant is the discovery of a novel, purely inorganic W24FeIII–superoxide cluster formed under ambient conditions. The modular wheel platform was employed to undertake two chemical space explorations, producing compounds 1–4: (C2H8N)10Na2[H6Fe(O2)W24O82] (1, {W24Fe}), (C2H8N)72Na16[H16Co8W200O660(H2O)40] (2, {W200Co8}), (C2H8N)72Na16[H16Ni8W200O660(H2O)40] (3, {W200Ni8}), and (C2H8N)14[H26W34V4O130] (4, {W34V4}), along with many other known species, such as simple Keggin clusters and 1D {W11M2+} chains

An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials

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

A Modular Programmable Inorganic Cluster Discovery Robot for the Discovery and Synthesis of Polyoxometalates

The exploration of complex multi-component chemical reactions leading to new clusters, where discovery requires both molecular self-assembly and crystallization, is a major challenge. This is because the systematic approach required for an experimental search is limited when the number of parameters in a chemical space becomes too large, restricting both exploration, and reproducibility. Herein, we present a synthetic strategy to systematically search a very large set of potential reactions, using an inexpensive, high-throughput platform; modular in terms of both hardware and software, and capable of running multiple reactions with in-line analysis; for the automation of inorganic and materials chemistry. The platform has been used to explore several inorganic chemical spaces to discover new, and reproduce known, tungsten-based, mixed transition-metal polyoxometalate clusters, giving a digital code allowing the easy repeat synthesis of the clusters. Among the many species identified in this work, most significantly is the discovery of a novel, purely inorganic W24FeIII-superoxide cluster formed under ambient conditions. The Modular Wheel Platform (MWP) was then employed to undertake two chemical space explorations producing compounds [1-4]: (C2H8N)10Na2[H6Fe(O2)W24O82(H2O)25] (1, {W24Fe}), (C2H8N)72Na16[H16Co8W200O660(H2O)40] (2, {W200Co8}), (C2H8N)72Na16[H16Ni8W200 O660-(H2O)40] (3, {W200Ni8}) and (C2H8N)14[H26W34V4O130] (4, {W34V4}), along with many other known species, for example simple Keggin clusters and 1D {W11M2+} chains. </p

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

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. </p

Networking Chemical Robots Using Twitter for #RealTimeChem

Herein we present a chemistry capable robot built with a standard set of hardware and software protocols that can be networked to coordinate many chemical experiments in real time, such that the different chemical reactions can be distributed over many sites simultaneously. We demonstrate how multiple chemical processes can be done with two internet connected robots collaboratively, exploring a set of azo-coupling reactions in a fraction of time needed for a single robot, as well as encoding and decoding information into a network of oscillating BZ reactions transferring a message between two different locations using chemical reactions. The system can also be used to assess the reproducibility of chemical reactions and discover new reaction outcomes using game playing to explore a list of reaction conditions not accessible when the robots instead take it in turn to each a pre-define reaction from a list.</p

Optimization of Formulations Using Robotic Experiments Driven by Machine Learning DoE

Formulated products are complex mixtures of ingredients whose time to market can be difficult to speed due to the lack of general predictable physical models for the desired properties. Here, we report the coupling of a machine learning classification algorithm with the Thompson sampling efficient multiobjective optimization (TSEMO) algorithm for the simultaneous optimization of continuous and discrete outputs. The methodology is successfully applied to the design of a formulated liquid product of commercial interest for which no physical models are available. Experiments are carried out in a semiautomated fashion using robotic platforms triggered by the machine learning algorithms. The procedure allows one to find nine suitable recipes meeting the customer-defined criteria within 15 working days, outperforming human intuition in the target performance of the formulations

Networking Chemical Robots Using Twitter for #RealTimeChem

<p>Herein we present a chemistry capable robot built with a standard set of hardware and software protocols that can be networked to coordinate many chemical experiments in real time, such that the different chemical reactions can be distributed over many sites simultaneously. We demonstrate how multiple chemical processes can be done with two internet connected robots collaboratively, exploring a set of azo-coupling reactions in a fraction of time needed for a single robot, as well as encoding and decoding information into a network of oscillating BZ reactions transferring a message between two different locations using chemical reactions. The system can also be used to assess the reproducibility of chemical reactions and discover new reaction outcomes using game playing to explore a list of reaction conditions not accessible when the robots instead take it in turn to each a pre-define reaction from a list.<br></p