  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. Combined with
additional prompts to select a subset of experimentally measured properties
from pure, single-crystalline bulk materials, this results in an automatically
extracted dataset that's larger and more diverse than the largest existing
human-curated database of experimental band gaps. Compared to the existing
human-curated database, we show the model trained on our extracted database
achieves a 19% reduction in the mean absolute error of predicted band gaps.
Finally, we demonstrate that LLMs are able to train models predicting band gap
on the extracted data, achieving an automated pipeline of data extraction to
materials property prediction.
