For composition-only property prediction (also known as structure-agnostic learning), compositions can be represented as composition-based feature vectors (CBFV) which are derived from element embeddings. Typically, the element embeddings are combined through pooling operations (commonly descriptive statistics) to make a CBFV which can be used as an input for machine learning. This enables material informatics practitioners to make machine-learning property predictions in the absence of explicit structural information.