Integrating Materials Representations Into Feature Engineering in Machine Learning for Crystalline Materials: From Local to Global Chemistry‐Structure Information Coupling

Integrating materials representations into feature engineering by rational design plays a critical role in determining the capability and accuracy of material property prediction via machine learning (ML). There still exists a lack of comprehensive classification and multi‐dimensional evaluation for many existing feature models that could guide model selection in applications and further development. This review systematically classifies feature construction methods for crystalline structures, emphasizing the coupling between chemical and structural information. We systematically discuss the geometric configurations, chemical attributes, and their intricate coupling mechanisms that can be leveraged for feature engineering. Furthermore, a comprehensive comparison is performed across multiple aspects including graph network representation, structural information embedding, chemistry‐structure information coupling, local versus global characteristics, long‐range versus short‐range description, algorithm compatibility with kernel function method or deep neural network, data size requirements, computational complexity, and interpretability mechanisms, thereby highlighting key variations in existing feature models and improving the physical interpretability of predictive models. To illustrate the integration of multi‐dimensional characteristics, the center‐environment (CE) feature model is introduced based on the coupling between local chemical and structural information of physical core‐shell structures. Within the CE model, the pre‐attention mechanism reorients focus from intricate details within complex ML algorithms to explicit feature models that depict physical core‐shell configurations. By minimizing data requirements while enhancing transparency in ML models, the CE feature provides a practical approach for developing efficient and accurate ML‐based predictions tailored for small‐data scenarios in materials science. This article is categorized under: Structure and Mechanism > Computational Materials Science Data Science > Artificial Intelligence/Machine Learning