Materials Acceleration Platforms are an emerging paradigm to accelerate materials discovery as an effort to develop technology solutions that can help address or mitigate climate change concerns. These platforms combine artificial intelligence, robotic systems, and high-performance computing to achieve autonomous experimentation. Nevertheless, their development faces challenges to achieve full autonomy. In this work, we present state-of-the-art robotic platforms and machine learning approaches for autonomous experimentation, their integration, and applications, particularly in the field of materials for clean energy technologies. Later, we discuss the challenges and suggest improvements to be considered in the endeavor to accomplish autonomous experimentation.

材料加速平台是一种新兴的范例，可以加速材料发现，以此来开发可以帮助解决或缓解气候变化问题的技术解决方案。这些平台结合了人工智能，机器人系统和高性能计算，可以实现自主实验。然而，他们的发展面临实现充分自治的挑战。在这项工作中，我们提出了用于自主实验，它们的集成和应用（特别是在清洁能源技术的材料领域）的最先进的机器人平台和机器学习方法。稍后，我们讨论了挑战，并提出了在完成自主实验中应考虑的改进措施。