So, you want to build a self-driving lab?

The scientific community has been exploring how to utilize #artificialintelligence and #labautomation to create "self-driving" (i.e., autonomous) scientific laboratories with the goal of accelerating the rate of scientific discovery (i.e., new materials and new phenomena). Like peering over the edge of a high-dive, getting into the space of self-driving labs, or even AI and automation in general, can be daunting. Recently, self-driving labs for #chemistry and #materialsscience have led to accelerated scientific discoveries related to climate change, energy, and medicine. Each of these labs are usually built over the course of several years by interdisciplinary teams, requiring capital in the millions. Of the reported self-driving labs, many have only ever initiated a single "glory flight" campaign.

Many naturally find themselves asking questions such as the following. What exactly is a self-driving lab? Where do I start? What are the benefits and risks? While the answers are highly dependent on circumstance and project visions, we will provide our perspectives on the hardware, software, personnel, and other requirements necessary to be sustainably successful in this space. This will cover topics such as flexible automation and #robotics, #computervision, #machinelearning, #datascience, and #workflowautomation. We will also describe the training efforts within the ‪@accelerationconsortium1598‬, which are intended to reduce the barrier-to-entry for and de-risk the adoption of self-driving labs through a range of online microcourses (https://ac-microcourses.readthedocs.io/) and in-person training experiences (https://ac-training-lab.readthedocs.io/).