Bias in Materials Science Research

This chapter examines how personal and systemic biases influence the categorization and evaluation of materials in research. It discusses the significance of data gathering and feature selection, especially in the context of machine learning models used in predicting materials properties. The conversation also challenges the belief that more data is always better, suggesting that smaller, curated datasets could lead to improved efficiency and accuracy in materials science.

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