Data-driven capabilities such as machine learning (ML) are rapidly growing in popularity in materials science and chemistry, but several prerequisites are crucial for repeatable success with these methods. We will walk through an end-to-end case study in which ML is applied to accelerate the design of nanomaterials, and focus attention on several enabling aspects such as experimental setup, data processing, and data management.

Machine learning-driven design of nanomaterials: Ingredients for success

2019 Fall National Meeting

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