Broadening Access to Accelerated Experimentation with Open-Source Hardware

Autonomous experimentation enables faster optimization of material properties by coupling a machine learning guided experimental design strategy with automated experimental execution workflows. Autonomous experimentation approaches have shown real advances in developing improved materials. However, implementing the required experimental automation workflows is a barrier to broader adoption of these systems. Traditionally, automated experimentation setups require expensive automation hardware and extensive technical expertise. Community driven open-source experimental automation hardware can lower these access barriers. One example is the Jubilee platform. Jubilee is composed of an open-source motion platform with researcher-developed tools and software to enable experimental workflows. Our lab has been contributing to the development of Jubilee and learning to use it in our research. Here, I will discuss the capabilities and possibilities of integrating Jubilee into automated experimentation. I will showcase these capabilities by discussing the development of an autonomous experimentation system for the optimization of silica nanoparticle morphologies. This system integrates automated sol-gel synthesis of silica nanoparticles with small-angle X-ray scattering characterization to autonomously optimize nanoparticle size distributions. Our work demonstrates that open hardware can make autonomous experimentation accessible to more researchers.