Scientific discovery is limited by finite experimental resources. Therefore, careful strategic planning is required when committing resources to an experiment. Often the decision to commit resources is based upon observations made from previous experiments. However real-world data is inherently noisy and often follows an underlying nonlinear trend. In such circumstances the decision to commit resources is unclear. Autonomous experimentation, where machine learning algorithms control an experimentation platform, is one approach that has the potential to deal with these issues and consequently could help drive scientific discoveries. In the context of applying autonomous experimentation to identify new behaviours from chemical or biological systems, the machine learning algorithms are limited by the capability of the hardware technology to generate on demand, complex mixtures from a wide range of chemicals. This limitation forms the basis for the work described in this thesis. Specifically this thesis documents the development of a hardware system which is designed to support scalability, is capable of automating processes, and is built from technology readily accessible to other researchers. The hardware system is derived from droplet microfluidic technology and allows for microscale biochemical samples of varying composition to be automatically created. During the development of the hardware system, technical challenges in fabrication, sensor system development, microfluidic design and mixing were encountered. Solutions to address these challenges were found and are presented as, fabrication techniques that enable integrated valve microfluidic devices to be created in a standard chemistry laboratory environment without need for sophisticated equipment, a compact UV photometer system built using optical semiconductor components, and a novel mixing strategy that increased the mixing efficiency of large droplets. Having addressed these technical challenges and in fulfilling the aims set out above, the work in this thesis has sufficiently improved hardware technology to free the machine learning algorithms from the constraint of working with just a few experimental variables.