Harnessing GPT-3.5 for text parsing in solid-state synthesis - case study of ternary chalcogenides

• Main Authors: Thway, Maung, Low, Andre Kai Yuan, Khetan, Samyak, Dai, Haiwen, Recatala-Gomez, Jose, Chen, Andy Paul, Hippalgaonkar, Kedar
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; GPT-3.5; Text parsing
• Online Access: https://hdl.handle.net/10356/174885
• Institution: Nanyang Technological University

Optimally doped single-phase compounds are necessary to advance state-of-the-art thermoelectric devices which convert heat into electricity and vice versa, requiring solid-state synthesis of bulk materials. For data-driven approaches to learn these recipes, it requires careful data curation from lar...