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.

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.

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.

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.

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.