Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification—A case study on nano-FeCu
Year: 2024
Type: article
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 approach offers a data-driven pathway for chemical material synthesis, demonstrating significant interdisciplinary applications.
Source: iScience
Authors: Dingding Cao, Mieow Kee Chan
Institutions: Guangdong Technology College, SEGi University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,150
OpenAlex ID: W4401997771