Materialswherelike

Materialswherelike is a term used in materials science and materials informatics to describe a set of materials that are similar to a chosen reference material according to predefined criteria. The concept is used as a framework for searching, comparing, and screening candidate materials in high-throughput experiments and data-driven design. While not a formal taxonomy, it informs methods for material retrieval based on structural, compositional, or property-related similarity.

Similarity is typically quantified by feature representations such as composition vectors, crystal structure descriptors, or learned embeddings. Common metrics include cosine similarity, Euclidean distance, or more advanced kernel and graph-based measures. Queries can be single-criteria, such as chemical composition, or multi-criteria, combining several properties like band gap, thermal conductivity, and stability.

Applications include virtual screening for materials with target properties, locating materials in the same family for experimental reuse, and guiding inverse design workflows where researchers seek materials that resemble a known good performer while meeting new constraints. The approach is often integrated into materials databases and discovery pipelines.

Limitations include sensitivity to the chosen representation and metrics, potential biases in training data, and the risk of conflating similarity with desirability. Materialswherelike is a tool to aid exploration rather than a guarantee of material performance; expert domain knowledge remains essential for interpretation of results.