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

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

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...

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Bibliographic Details
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: Journal Article
Language:English
Published: 2024
Subjects:
Engineering
GPT-3.5
Text parsing
Online Access:https://hdl.handle.net/10356/174885

Internet

https://hdl.handle.net/10356/174885

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