This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modelling. Part two reflects on these case studies and identifies a set of open challenges for the field.

中文翻译：

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化学科学中的自主发现第一部分：进展

这篇由两部分组成的评论探讨了自动化如何对化学科学中发现的不同方面做出贡献。在第一部分中，我们描述了物理物质（分子、材料、设备）、过程和模型的发现的分类，以及它们如何统一为搜索问题。然后，我们介绍了一系列与评估自主程度相关的问题和考虑因素。最后，我们描述了许多由合成化学、药物发现、无机化学和材料科学领域的计算机辅助和自动化加速或导致的发现的案例研究。这些说明了硬件自动化和机器学习的快速进步如何继续改变实验和建模的性质。