Routescore: Punching the Ticket to More Efficient Materials Development

16 July 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Self-driving labs, in the form of automated experimentation platforms guided by machine learning algorithms have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving labs. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization), and to simulate a self-driving lab that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ~3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and generality of our approach in mixed synthetic planning and optimization.


machine learning
self-driving lab
automated synthesis


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