Progress in data-driven self-driving laboratories for solving materials grand challenges has accelerated with the advent of machine learning, robotics and automation but usually designed with specific materials and processes in mind. To develop the next generation of Materials Acceleration Platforms (MAPs), we propose a unified framework to enable collaboration between MAPs, leveraging on object-oriented programming principles using which research groups around the world would be able to effectively evolve experimental workflows. We demonstrate the framework via three experimental case studies from disparate fields to illustrate the evolution of, and seamless integration between workflows, promoting efficient resource utilisation and collaboration. Moving forward, we project our framework on three other research areas that would benefit from such an evolving workflow. Through the wide adoption of our framework, we envision a collaborative, connected, global community of MAPs working together to solve scientific grand challenges.
Minor formatting and figure placement changes. Inclusion of funding information in acknowledgments.