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This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science.
Theory of superconductivity
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A survey of named entity recognition and classification
David Nadeau and Satoshi Sekine · 2007
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Microstructure and properties of high-temperature superconductors
Ivan A Parinov · 2013
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Exploration of new superconductors and functional materials, and fabrication of superconducting tapes and wires of iron pnictides
Hideo Hosono, Keiichi Tanabe, Eiji Takayama-Muromachi, Hiroshi Kageyama, Shoji Yamanaka, Hiroaki Kumakura, Minoru Nohara, Hidenori Hiramatsu, and Satoru Fujitsu · 2015
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A polymer dataset for accelerated property prediction and design
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An open experimental database for exploring inorganic materials
Andriy Zakutayev, Nick Wunder, Marcus Schwarting, John D Perkins, Robert White, Kristin Munch, William Tumas, and Caleb Phillips · 2018
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Data-driven design of metal–organic frameworks for wet flue gas co2 capture
Peter G Boyd, Arunraj Chidambaram, Enrique García-Díez, Christopher P Ireland, Thomas D Daff, Richard Bounds, Andrzej Gładysiak, Pascal Schouwink, Seyed Mohamad Moosavi, M Mercedes Maroto-Valer, et al · 2019
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Automatic identification and normalisation of physical measurements in scientific literature
Luca Foppiano, Laurent Romary, Masashi Ishii, and Mikiko Tanifuji · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych · 2019
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SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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Machine learning and data mining in materials science, 2020
Norbert Huber, Surya R Kalidindi, Benjamin Klusemann, and Christian J Cyron · 2020
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Data augmentation in microscopic images for material data mining
Boyuan Ma, Xiaoyan Wei, Chuni Liu, Xiaojuan Ban, Haiyou Huang, Hao Wang, Weihua Xue, Stephen Wu, Mingfei Gao, Qing Shen, Michele Mukeshimana, Adnan Omer Abuassba, Haokai Shen, and Yanjing Su · 2020
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Electron doping of the iron-arsenide superconductor cefeaso controlled by hydrostatic pressure
K Mydeen, Anton Jesche, K Meier-Kirchner, U Schwarz, C Geibel, H Rosner, and Michael Nicklas · 2020
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Advances in scientific literature mining for interpreting materials characterization
Gilchan Park and Line Pouchard · 2021
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Big data mining and classification of intelligent material science data using machine learning
Swetha Chittam, Balakrishna Gokaraju, Zhigang Xu, Jagannathan Sankar, and Kaushik Roy · 2021
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Gpt-3 models are poor few-shot learners in the biomedical domain
Milad Moradi, Kathrin Blagec, Florian Haberl, and Matthias Samwald · 2021
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SemEval-2021 task 8: MeasEval – extracting counts and measurements and their related contexts
Corey Harper, Jessica Cox, Curt Kohler, Antony Scerri, Ron Daniel Jr., and Paul Groth · 2021
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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On the planning abilities of large language models–a critical investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Pearl: Prompting large language models to plan and execute actions over long documents
Simeng Sun, Yang Liu, Shuohang Wang, Chenguang Zhu, and Mohit Iyyer · 2023
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ChatGPT: Jack of all trades, master of none
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, Anna Kocoń, Bartłomiej Koptyra, Wiktoria Mieleszczenko-Kowszewicz, Piotr Miłkowski, Marcin Oleksy, Maciej Piasecki, Łukasz Radliński, Konrad Wojtasik, Stanisław Woźniak, and Przemysław Kazienko · 2023
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Supermat: construction of a linked annotated dataset from superconductors-related publications
Luca Foppiano, Thaer Dieb, Akira Suzuki, Pedro Castro, Suguru Iwasaki, Asuza Uzuki, Miren Echevarria, Yan Meng, Kensei Terashima, Laurent Romary, Yoshihiko Takano, and Masashi Ishii · 2021
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Machine learning–enabled high-entropy alloy discovery
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Accelerating materials discovery using artificial intelligence, high performance computing and robotics
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Galactica: A large language model for science
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Yubo Ma, Yixin Cao, YongChing Hong, and Aixin Sun · 2023
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Yes but.. can chatgpt identify entities in historical documents?
Carlos-Emiliano González-Gallardo, Emanuela Boros, Nancy Girdhar, Ahmed Hamdi, Jose G Moreno, and Antoine Doucet · 2023
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Prompt engineering of gpt-4 for chemical research: what can/cannot be done?
Kan Hatakeyama-Sato, Naoki Yamane, Yasuhiko Igarashi, Yuta Nabae, and Teruaki Hayakawa · 2023
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Using gpt-4 in parameter selection of polymer informatics: improving predictive accuracy amidst data scarcity and ‘ugly duckling’dilemma
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi, and Kenichi Oyaizu · 2023
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Automatic extraction of materials and properties from superconductors scientific literature
Luca Foppiano, Pedro Castro, Pedro Suarez, Kensei Terashima, Yoshihiko Takano, and Masashi Ishii · 2023
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Pattern matching: the gestalt approach
W. Ratcliff John · 2024
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Matscire: Leveraging pointer networks to automate entity and relation extraction for material science knowledge-base construction
Ankan Mullick, Akash Ghosh, G Sai Chaitanya, Samir Ghui, Tapas Nayak, Seung-Cheol Lee, Satadeep Bhattacharjee, and Pawan Goyal · 2024
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