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.

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.

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.

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.