Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network.

We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (p-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of 6 equiv of TosMIC in a "multistep, single-substrate" cascade reaction yielding a trimeric product in high yield (47% unoptimized) with the formation of five new C-C bonds involving sp-sp2 and sp-sp3 carbon centers. An analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of a reactivity-first robotic discovery of unknown reaction methodologies without requiring human input.

An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge.

We present a chemical discovery robot for the efficient and reliable discovery of supramolecular architectures through the exploration of a huge reaction space exceeding ten billion combinations. The system was designed to search for areas of reactivity found through autonomous selection of the reagent types, amounts, and reaction conditions aiming for combinations that are reactive. The process consists of two parts where reagents are mixed together, choosing from one type of aldehyde, one amine and one azide (from a possible family of two amines, two aldehydes and four azides) with different volumes, ratios, reaction times, and temperatures, whereby the reagents are passed through a copper coil reactor. Next, either cobalt or iron is added, again from a large number of possible quantities. The reactivity was determined by evaluating differences in pH, UV-Vis, and mass spectra before and after the search was started. The algorithm was focused on the exploration of interesting regions, as defined by the outputs from the sensors, and this led to the discovery of a range of 1-benzyl-(1,2,3-triazol-4-yl)-N-alkyl-(2-pyridinemethanimine) ligands and new complexes: [Fe(L1 )2 ](ClO4 )2 (1); [Fe(L2 )2 ](ClO4 )2 (2); [Co2 (L3 )2 ](ClO4 )4 (3); [Fe2 (L3 )2 ](ClO4 )4 (4), which were crystallised and their structure confirmed by single-crystal X-ray diffraction determination, as well as a range of new supramolecular clusters discovered in solution using high-resolution mass spectrometry.

Designing Algorithms To Aid Discovery by Chemical Robots.

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery.

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.

Approach to classify, separate, and enrich objects in groups using ensemble sorting.

The sorting of objects into groups is a fundamental operation, critical in the preparation and purification of populations of cells, crystals, beads, or droplets, necessary for research and applications in biology, chemistry, and materials science. Most of the efforts exploring such purification have focused on two areas: the degree of separation and the measurement precision required for effective separation. Conventionally, achieving good separation ultimately requires that the objects are considered one by one (which can be both slow and expensive), and the ability to measure the sorted objects by increasing sensitivity as well as reducing sorting errors. Here we present an approach to sorting that addresses both critical limitations with a scheme that allows us to approach the theoretical limit for the accuracy of sorting decisions. Rather than sorting individual objects, we sort the objects in ensembles, via a set of registers which are then in turn sorted themselves into a second symmetric set of registers in a lossless manner. By repeating this process, we can arrive at high sorting purity with a low set of constraints. We demonstrate both the theory behind this idea and identify the critical parameters (ensemble population and sorting time), and show the utility and robustness of our method with simulations and experimental systems spanning several orders of scale, sorting populations of macroscopic beads and microfluidic droplets. Our method is general in nature and simplifies the sorting process, and thus stands to enhance many different areas of science, such as purification, enrichment of rare objects, and separation of dynamic populations.

Adaptive artificial evolution of droplet protocells in a 3D-printed fluidic chemorobotic platform with configurable environments.

Evolution via natural selection is governed by the persistence and propagation of living things in an environment. The environment is important since it enabled life to emerge, and shapes evolution today. Although evolution has been widely studied in a variety of fields from biology to computer science, still little is known about the impact of environmental changes on an artificial chemical evolving system outside of computer simulations. Here we develop a fully automated 3D-printed chemorobotic fluidic system that is able to generate and select droplet protocells in real time while changing the surroundings where they undergo artificial evolution. The system is produced using rapid prototyping and explicitly introduces programmable environments as an experimental variable. Our results show that the environment not only acts as an active selector over the genotypes, but also enhances the capacity for individual genotypes to undergo adaptation in response to environmental pressures.

An autonomous organic reaction search engine for chemical reactivity.

The exploration of chemical space for new reactivity, reactions and molecules is limited by the need for separate work-up-separation steps searching for molecules rather than reactivity. Herein we present a system that can autonomously evaluate chemical reactivity within a network of 64 possible reaction combinations and aims for new reactivity, rather than a predefined set of targets. The robotic system combines chemical handling, in-line spectroscopy and real-time feedback and analysis with an algorithm that is able to distinguish and select the most reactive pathways, generating a reaction selection index (RSI) without need for separate work-up or purification steps. This allows the automatic navigation of a chemical network, leading to previously unreported molecules while needing only to do a fraction of the total possible reactions without any prior knowledge of the chemistry. We show the RSI correlates with reactivity and is able to search chemical space using the most reactive pathways.

Towards heterotic computing with droplets in a fully automated droplet-maker platform.

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations.

Observation of the isotope effect in sub-kelvin reactions.

Quantum phenomena in the translational motion of reactants, which are usually negligible at room temperature, can dominate reaction dynamics at low temperatures. In such cold conditions, even the weak centrifugal force is enough to create a potential barrier that keeps reactants separated. However, reactions may still proceed through tunnelling because, at low temperatures, wave-like properties become important. At certain de Broglie wavelengths, the colliding particles can become trapped in long-lived metastable scattering states, leading to sharp increases in the total reaction rate. Here, we show that these metastable states are responsible for a dramatic, order-of-magnitude-strong, quantum kinetic isotope effect by measuring the absolute Penning ionization reaction rates between hydrogen isotopologues and metastable helium down to 0.01 K. We demonstrate that measurements of a single isotope are insufficient to constrain ab initio calculations, making the kinetic isotope effect in the cold regime necessary to remove ambiguity among possible potential energy surfaces.

Stopping paramagnetic supersonic beams: the advantage of a co-moving magnetic trap decelerator.

The long standing goal of chemical physics is finding a convenient method to create slow and cold beams intense enough to observe chemical reactions in the temperature range of a few Kelvin. We present an extensive numerical analysis of our moving magnetic trap decelerator showing that a 3D confinement throughout the deceleration process enables deceleration of almost all paramagnetic particles within the original supersonic expansion to stopping velocities. We show that the phase space region containing the decelerating species is larger by two orders of magnitude as compared to other available deceleration methods.