Yi, Gyeong Hoon Choi, Jiwoo Song, Hyeongyun Miano, Olivia Choi, Jaewoong Bang, Kihoon Lee, Byungju Sohn, Seok Su Buttler, David Hiszpanski, Anna Han, Sang Soo Kim, Donghun 2025-03-19T15:31:18Z 2025-03-19T15:31:18Z 2025-03-19 2025-04 https://pubs.kist.re.kr/handle/201004/151918 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, the study presents 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 achieves 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, the study presents a Pareto-front mapping where the few-shot learning method is 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. English Wiley-VCH Verlag MaTableGPT: GPT-Based Table Data Extractor from Materials Science Literature Article 10.1002/advs.202408221 1 Advanced Science, v.12, no.16 Advanced Science 12 16 Y scie scopus 001405309300001 2-s2.0-85215658421 Chemistry, Multidisciplinary Nanoscience & Nanotechnology Materials Science, Multidisciplinary Chemistry Science & Technology - Other Topics Materials Science Article OXYGEN EVOLUTION REDUCTION CATALYST GPT large language models literature mining machine learning materials science table data extraction water splitting catalysis