Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.

A Reactivity First Approach to Autonomous Discovery of New Chemistry

<p></p><p></p><p>We present a robotic chemical discovery system capable of learning the generalized notion of reactivity using a neural network model that can abstract the reactivity from the identity of the reagents. The system is controlled using an algorithm that works in conjunction with this learned knowledge, the robot was able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown datasets, regardless the identity of the starting materials. The system identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, but also some unpredictable reactions that yielded new molecules. The search was done within a budget of 15 inputs combined in 1018 reactions, which allowed us not only to discover a new photochemical reaction, but also a new reactivity mode for a well-known reagent (<i>p</i>-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of six equivalents of TosMIC in a ‘multi-step, single-substrate’ cascade reaction yielding a trimeric product in high yield (47% unoptimized) with formation of five new C-C bonds involving <i>sp</i>-<i>sp²</i> and <i>sp</i>-<i>sp³</i> carbon centres. Analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of reactivity-first robotic discovery of unknown reaction methodologies without requiring human input.</p><br><p></p><p></p>