Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction

Understand

Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials.

• However, such models require training data.
• While automated data extraction from scientific literature has potential, current auto-generated datasets often lack sufficient accuracy and critical structural and processing details of materials that influence the properties.
• Using band gap as an example, we demonstrate Large language model (LLM)-prompt-based extraction yields an order of magnitude lower error rate.