  Efficiently extracting data from tables in the scientific literature is
pivotal for building large-scale databases. However, the tables reported in
materials science papers exist in highly diverse forms; thus, rule-based
extractions are an ineffective approach. To overcome this challenge, we present
MaTableGPT, which is a GPT-based table data extractor from the materials
science literature. MaTableGPT features key strategies of table data
representation and table splitting for better GPT comprehension and filtering
hallucinated information through follow-up questions. When applied to a vast
volume of water splitting catalysis literature, MaTableGPT achieved an
extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive
evaluations of the GPT usage cost, labeling cost, and extraction accuracy for
the learning methods of zero-shot, few-shot and fine-tuning, we present a
Pareto-front mapping where the few-shot learning method was found to be the
most balanced solution owing to both its high extraction accuracy (total F1
score>95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of
10 I/O paired examples). The statistical analyses conducted on the database
generated by MaTableGPT revealed valuable insights into the distribution of the
overpotential and elemental utilization across the reported catalysts in the
water splitting literature.
