Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification-A case study on nano-FeCu.

ABSTRACT: Nanoparticle synthesis is complex, influenced by multiple variables including reagent selection. This study introduces a specialized corpus focused on "Fe, Cu, synthesis" to train a domain-specific word embedding model using natural language processing (NLP) in an unsupervised environment. Evaluation metrics included average cosine similarity, visual analysis via t-distributed stochastic neighbor embedding (t-SNE), synonym analysis, and analogy reasoning analysis. Results indicate a strong correlation between learning rate and cosine similarity, with enhanced chemical specificity in the tailored model compared to general models. The framework facilitates rapid identification of potential reagents for nano-FeCu synthesis, enhancing precision in nanomaterial research. This innovative approac

SUBMITTER: Cao D 

PROVIDER: S-EPMC11417335 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature