This white paper examines why applying machine learning to materials and chemical development is fundamentally different from typical AI use cases and how these challenges can be overcome. Unlike traditional machine learning, materials data is often sparse, inconsistent, and non-standardized, with limited datasets and strong reliance on domain expertise. The paper explains why generic, off-the-shelf AI tools require extensive customization to work effectively in this space. Drawing on Citrine’s experience, it outlines approaches to handling small datasets, incorporating scientific knowledge, and building models that deliver reliable insights, helping organizations successfully apply machine learning to complex materials problems.

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