The slow process of discovering new materials with enhanced properties, long term durability and ease of device integration is the critical bottleneck in the green transition. Automated materials discovery and optimisation mark a breakthrough in this direction often captured by the general concept of a self-driving laboratory (SDL) and in a broader context as Materials Acceleration Platforms (MAPs) . This approach involves autonomous or closed-loop experimentation, integrating robotics and Artificial Intelligence (AI) to autonomously conduct, assess, and steer experiments using data analysed through machine learning (ML). Such platforms transcend conventional lab automation by solving problems without the need for human interaction.