Product design for formulations is an active and challenging area of research. The new challenges of a fast‐paced market, products of increasing complexity, and practical translation of sustainability paradigms require to re‐examine the existing theoretical frameworks to include the advantages deriving from the new reality of digitalization of business and research. In this work, we review the existing approaches, clearly stating the role of automation and machine‐learning‐guided optimization in the broader framework. Moving from this, we review the state of the art of automated hardware and software for formulated product design, and identify the open challenges for future research. Perspectives are given on the emerging fields of automated discovery, scale‐up, and multistage optimization, and a unitary picture of the existing connections is provided, in the general context of a completely digital R&D workflow.

中文翻译：

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配方设计和开发中的自动化机器人平台

制剂的产品设计是一个活跃而富有挑战性的研究领域。快节奏的市场所带来的新挑战，日益复杂的产品以及可持续性范式的实际翻译都需要重新审视现有的理论框架，以包括从商业和研究数字化的新现实中获得的优势。在这项工作中，我们回顾了现有的方法，清楚地说明了自动化和机器学习指导的优化在更广泛的框架中的作用。在此基础上，我们回顾了用于配方产品设计的自动化硬件和软件的最新状况，并确定了未来研究面临的开放性挑战。展望了自动发现，放大和多阶段优化等新兴领域，