Features

With the growth of AI and DL comes new opportunities for emerging applications, finds Robert Roe

Gemma Church examines how simulation and modelling aid the increasingly diverse renewable energy field

 Sophia Ktori considers how software integration helps ensure scientists work efficiently in the laboratory

In this guide we have attempted to coalesce much of the information required in order to design and implement a smart laboratory or, at the very least, to begin the process of laboratory automation. While it may seem like a challenging prospect, the underlying principles are simple and focused on crafting a strategy that will enable more productivity and insight to be generated from scientific research

This chapter looks at how to build a smart laboratory; what approaches to take; and how to deal with potential problems. Becoming ‘smart’ takes time, not only due to the level of investment required, but also because of the impact of change and the need to consider legacy requirements.