Comparison of software frameworks for Self driving laboratories

Project description

This project presents a comparative analysis of self-driving laboratory frameworks, instrumental in automating experiments in materials science. Leveraging AI, these frameworks optimize research efficiency, facilitating breakthroughs in various applied sciences. The study meticulously evaluates eight frameworks, considering factors like hardware control and data management. Essential for laboratories aiming to integrate machine learning and robotics, the project's findings serve as a decision-making tool for selecting the most suitable framework for specific research needs, ultimately propelling the innovation of automated experimental setups.

Context

The project's context revolves around the comparison of software control systems for self-driving labs, which are critical for automating high-throughput experiments in material science. These systems use AI algorithms to optimize experiments, which is vital for advancing fields such as energy, health, and environment. The paper is part of the Robotlab project, aiming to develop advanced molecular systems through automation and AI, collaborating with various research institutions.

Results

The study's results provide a detailed comparison of eight self-driving lab frameworks, evaluating their features, capabilities, and performance. Each framework is analyzed based on criteria such as hardware control, experiment planning, data storage, and user interaction. The frameworks range from ChemOS, known for its modularity and autonomous experimentation, to commercial platforms like SDLabs, which integrates ML, robotics, and cloud computing. The paper offers a multi-criteria decision matrix for selecting suitable systems for specific applications, aiding in the advancement of automated laboratories.

TRLevel 3