Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments

Traditionally, chemists have relied
on years of training and accumulated
experience in order to discover new molecules. But the space of possible
molecules is so vast that only a limited exploration with the traditional
methods can be ever possible. This means that many opportunities for
the discovery of interesting phenomena have been missed, and in addition,
the inherent variability of these phenomena can make them difficult
to control and understand. The current state-of-the-art is moving
toward the development of automated and eventually fully autonomous
systems coupled with in-line analytics and decision-making algorithms.
Yet even these, despite the substantial progress achieved recently,
still cannot easily tackle large combinatorial spaces, as they are
limited by the lack of high-quality data. Herein, we explore the utility
of active learning methods for exploring the chemical space by comparing
the collaboration between human experimenters with an algorithm-based
search against their performance individually to probe the self-assembly
and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show
that the robot-human teams are able to increase the prediction accuracy
to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone
and 66.3 ± 1.8% from only the human experimenters demonstrating
that human-robot teams can beat robots or humans working alone.