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

On the fly multi-modal observation of ligand synthesis and complexation of Cu complexes in flow with ‘benchtop’ NMR and mass spectrometry

Exploring complex chemical systems requires reproducible and controllable ways to access non-equilibrium conditions. Herein we present a programmable flow system that can do both ligand synthesis and complexation on the fly, and the conditions of the reaction can be monitored using two simultaneous techniques, namely NMR and mass spectrometry. By using this approach we monitored the formation of unknown complexes, followed by crystallization that resulted in the characterisation of their structures giving 5 new compounds (4 isolated and fully characterised) which can be formulated as: Cu2(L1)4(μ-CO3)](BF4)2 (2); [Cu3(L1)6(μ-CO3)](PF6)2(OH)2 (3) [Cu2(L2)2](BF4)2 (4) and [Cu(L2)2](BF4)2·CH3CN (5)

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

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

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

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

Few studies have explored the effect of a changing environment on artificial chemical evolution. Here, the authors develop an evolutionary platform that alters the physical environment of droplet protocells, showing that a population of simple chemical species can adapt to its surroundings, in analogy to natural evolution

An autonomous chemical robot discovers the rules of supramolecular 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

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

A Moving Magnetic Trap Decelerator: a New Source for Cold Atoms and Molecules

We present an experimental realization of a moving magnetic trap decelerator, where paramagnetic particles entrained in a cold supersonic beam are decelerated in a co-moving magnetic trap. Our method allows for an efficient slowing of both paramagnetic atoms and molecules to near stopping velocities. We show that under realistic conditions we will be able to trap and decelerate a large fraction of the initial supersonic beam. We present our first results on deceleration in a moving magnetic trap by bringing metastable neon atoms to near rest. Our estimated phase space volume occupied by decelerated particles at final velocity of 50 m/s shows an improvement of two orders of magnitude as compared to currently available deceleration techniques

How to explore chemical space using algorithms and automation

Although extending the reactivity of a given class of molecules is relatively straightforward, the discovery of genuinely new reactivity and the molecules that result is a wholly more challenging problem. If new reactions can be considered unpredictable using current chemical knowledge, then we suggest that they are not merely new but also novel. Such a classification, however, requires an expert judge to have access to all current chemical knowledge or risks a lack of information being interpreted as unpredictability. Here, we describe how searching chemical space using automation and algorithms improves the probability of discovery. The former enables routine chemical tasks to be performed more quickly and consistently, while the latter uses algorithms to facilitate the searching of chemical knowledge databases. Experimental systems can also be developed to discover novel molecules, reactions and mechanisms by augmenting the intuition of the human expert. In order to find new chemical laws, we must seek to question current assumptions and biases. Accomplishing that involves using two areas of algorithmic approaches: algorithms to perform searches, and more general machine learning and statistical modelling algorithms to predict the chemistry under investigation. We propose that such a chemical intelligence approach is already being used and that, in the not-too-distant future, the automated chemical reactor systems controlled by these algorithms and monitored by a sensor array will be capable of navigating and searching chemical space more quickly, efficiently and, importantly, without bias. This approach promises to yield not only new molecules but also unpredictable and thus novel reactivity