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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.

Despite huge potential, automation of synthetic chemistry has only made incremental progress over the past few decades. We present an automatically executable chemical reaction database of 100 molecules representative of the range of reactions found in contemporary organic synthesis. These reactions include transition metal–catalyzed coupling reactions, heterocycle formations, functional group interconversions, and multicomponent reactions. The chemical reaction codes or χDLs for the reactions have been stored in a database for version control, validation, collaboration, and data mining. Of these syntheses, more than 50 entries from the database have been downloaded and robotically run in seven modular ChemPU’s with yields and purities comparable to those achieved by an expert chemist. We also demonstrate the automatic purification of a range of compounds using a chromatography module seamlessly coupled to the platform and programmed with the same language.

The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab., The search for life in the universe is difficult due to issues with defining signatures of living systems. Here, the authors present an approach based on the molecular assembly number and tandem mass spectrometry that allows identification of molecules produced by biological systems, and use it to identify biosignatures from a range of samples, including ones from outer space.

We present a cyber-physical robot for the exploration, discovery, and optimisation of nanostructures which is driven by real-time spectroscopic feedback, theory and machine learning algorithms that control the reaction conditions and allow the selective templating of the reactions. This approach allows the transfer of materials as seeds, as well as digital information, 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 UV-Vis characterisation led to the discovery of five classes of nanoparticles by only needing to perform ca. one thousand from a total of ca. >1023 possible experiments. The platform optimises the nanostructures with desired optical properties by combing experiments and scattering simulations to achieve a yield of up to 95% and the synthesis code is outputted in a universal format using Chemical Description Language(χDL) together with the analytical data to produce a unique digital signature to aid and confirm that the synthesis is universally reproducible.

Summary 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.

The search for evidence of life elsewhere in the universe is hard because it is not obvious what signatures are unique to life. Here we postulate that complex molecules found in high abundance are universal biosignatures as they cannot form by chance. To explore this, we developed the first intrinsic measure of molecular complexity that can be experimentally determined, and this is based upon a new approach called assembly theory which gives the molecular assembly number (MA) of a given molecule. MA allows us to compare the intrinsic complexity of molecules using the minimum number of steps required to construct the molecular graph starting from basic objects, and a probabilistic model shows how the probability of any given molecule forming randomly drops dramatically as its MA increases. To map chemical space, we calculated the MA of ca. 2.5 million compounds, and collected data which showed the complexity of a molecule can be experimentally determined by using three independent techniques including infra-red spectroscopy, nuclear magnetic resonance, and by fragmentation in a mass spectrometer, and this data has an excellent corelation with the values predicted from our assembly theory. We then set out to see if this approach could allow us to identify molecular biosignatures with a set of diverse samples from around the world, outer space, and the laboratory including prebiotic soups. The results show that there is a non-living to living threshold in MA complexity and the higher the MA for a given molecule, the more likely that it had to be produced by a biological process. This work demonstrates it is possible to use this approach to build a life detection instrument that could be deployed on missions to extra-terrestrial locations to detect biosignatures, map the extent of life on Earth, and be used as a molecular complexity scale to quantify the constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital if we are going to find new life elsewhere in the universe or create de-novo life in the lab.

No synthetic chemical system can produce complex oligomers with fidelities comparable to biological systems. To bridge this gap, chemists must be able to characterise synthetic oligomers. Currently there are no tools for identifying synthetic oligomers with sequence resolution. Herein, we present a system that allows us to do omics-level sequencing for synthetic oligomers and use this to explore unconstrained complex mixtures. The system, Oligomer-Soup-Sequencing (OLIGOSS), can sequence individual oligomers in heterogeneous and polydisperse mixtures from tandem mass spectrometry (MS/MS) data. Unlike existing software, OLIGOSS can sequence oligomers with different backbone chemistries. Using an input file format, OLIG, that formalizes the set of abstract properties, any MS/MS fragmentation pathway can be defined. This has been demonstrated on four model systems of linear oligomers. OLIGOSS can screen large sequence spaces, enabling reliable sequencing of synthetic oligomeric mixtures, with false discovery rates (FDRs) of 0-1.1%, providing sequence resolution comparable to bioinformatic tools.

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., The ability to discover and optimise the synthesis of inorganic nanomaterials has significant impact on various fields, from sensing to medicine. Here, the authors use a genetic algorithm to drive a robotic platform toward a pre-defined, spectroscopic goal in order to discover and optimise the conditions for several nanoparticle shapes.

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., A low-cost, automated modular wheel platform (MWP) is described and used to explore a chemical space. Using a digital synthetic code, the MWP can reproduce known polyoxometalate syntheses and discover novel clusters.

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.

Paper in, product out A typical chemist running a known reaction will start by finding the method described in a published paper. Mehr et al. report a software platform that uses natural language processing to translate the organic chemistry literature directly into editable code, which in turn can be compiled to drive automated synthesis of the compound in the laboratory. The synthesis procedure is intended to be universally applicable to robotic systems operating in a batch reaction architecture. The full process is demonstrated for synthesis of an analgesic as well as common oxidizing and fluorinating agents. Science , this issue p. 101

Clear directions for a robotic platform The chemistry literature contains more than a century's worth of instructions for making molecules, all written by and for humans. Steiner et al. developed an autonomous compiler and robotic laboratory platform to synthesize organic compounds on the basis of standardized methods descriptions (see the Perspective by Milo). The platform comprises conventional equipment such as round-bottom flasks, separatory funnels, and a rotary evaporator to maximize its compatibility with extant literature. The authors showcase the system with short syntheses of three common pharmaceuticals that proceeded comparably to manual synthesis. Science , this issue p. eaav2211 ; see also p. 122

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses., Competing Interests: Conflict of interest. None declared