Machine learning in experimental materials chemistry

Abstract The development of advanced materials is an important aspect of modern life. However, the discovery of novel materials involves searching the vast chemical space to find materials with desired properties. Recent developments in the applications of Machine Learning (ML) in materials chemistry show promise to accelerate the material discovery process. In this perspective article, we highlight the importance of ML in materials chemistry. We discuss few examples of ML applications in synthesis, characterization, and predicting activities of materials. Finally, we discuss the challenges in this field and how the progress in ML in chemistry is leveraged together with advanced robotics to perform automated optimization of material discovery.

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