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There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs).
S. P. Ong, W. D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V. L. Chevrier, K. A. Persson, and G. Ceder, Python materials genomics (pymatgen): A robust, open-source python library for materials analysis, Computational Materials Science 68
2013
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M. C. Swain and J. M. Cole, Chemdataextractor: A toolkit for automated extraction of chemical information from the scientific literature, Journal of Chemical Information and Modeling 56
2016
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E. Kim, K. Huang, A. Saunders, A. McCallum, G. Ceder, and E. Olivetti, Materials synthesis insights from scientific literature via text extraction and machine learning, Chemistry of Materials 29
2017
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S. Gorsse, M. Nguyen, O. Senkov, and D. Miracle, Database on the mechanical properties of high entropy alloys and complex concentrated alloys, Data in Brief 21
2018
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E. J. Beard, G. Sivaraman, A. Vazquez-Mayagoitia, et al. , Comparative dataset of experimental and computational attributes of uv/vis absorption spectra, Sci Data 6
2019
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Z. Jensen, E. Kim, S. Kwon, T. Z. H. Gani, Y. Román-Leshkov, M. Moliner, A. Corma, and E. Olivetti, A machine learning approach to zeolite synthesis enabled by automatic literature data extraction, ACS Central Science 5
2019
Earlier work this paper cites.
E. A. Olivetti, J. M. Cole, E. Kim, O. Kononova, G. Ceder, T. Y.-J. Han, and A. M. Hiszpanski, Data-driven materials research enabled by natural language processing and information extraction, Applied Physics Reviews 7
2020
Earlier work this paper cites.
C. Court and J. Cole, Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning, npj Comput Mater 6
2020
Earlier work this paper cites.
J. E. Saal, A. O. Oliynyk, and B. Meredig, Machine learning in materials discovery: Confirmed predictions and their underlying approaches, Annual Review of Materials Research 50
2020
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D. Morgan and R. Jacobs, Opportunities and challenges for machine learning in materials science, Annual Review of Materials Research 50
2020
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E. Kim, Z. Jensen, A. van Grootel, K. Huang, M. Staib, S. Mysore, H.-S. Chang, E. Strubell, A. McCallum, S. Jegelka, and E. Olivetti, Inorganic materials synthesis planning with literature-trained neural networks, Journal of Chemical Information and Modeling 60
2020
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, Language models are few-shot learners 10.48550/ARXIV.2005.14165 (2020)
2020
Earlier work this paper cites.
C. K. H. Borg, C. Frey, J. Moh, T. M. Pollock, S. Gorsse, D. B. Miracle, O. N. Senkov, B. Meredig, and J. E. Saal, Expanded dataset of mechanical properties and observed phases of multi-principal element alloys, Scientific Data 7
2020
Earlier work this paper cites.
J. Mavračić, C. J. Court, T. Isazawa, S. R. Elliott, and J. M. Cole, Chemdataextractor 2.0: Autopopulated ontologies for materials science, Journal of Chemical Information and Modeling 61
2021
Cited alongside, same era.
C. Karpovich, Z. Jensen, V. Venugopal, and E. Olivetti, Inorganic synthesis reaction condition prediction with generative machine learning 10.48550/ARXIV.2112.09612 (2021)
2021
Cited alongside, same era.
A. B. Georgescu, P. Ren, A. R. Toland, S. Zhang, K. D. Miller, D. W. Apley, E. A. Olivetti, N. Wagner, and J. M. Rondinelli, Database, features, and machine learning model to identify thermally driven metal–insulator transition compounds, Chemistry of Materials 33
2021
Cited alongside, same era.
O. Kononova, T. He, H. Huo, A. Trewartha, E. A. Olivetti, and G. Ceder, Opportunities and challenges of text mining in materials research, iScience 24
2021
Cited alongside, same era.
S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. V. Lin, T. Mihaylov, M. Ott, S. Shleifer, K. Shuster, D. Simig, P. S. Koura, A. Sridhar, T. Wang, and L. Zettlemoyer, Opt: Open pre-trained transformer language models 10.48550/ARXIV.2205.01068 (2022)
2022
Later among the works it cites.
A. Dunn, J. Dagdelen, N. Walker, S. Lee, A. S. Rosen, G. Ceder, K. Persson, and A. Jain, Structured information extraction from complex scientific text with fine-tuned large language models 10.48550/ARXIV.2212.05238 (2022)
2022
Later among the works it cites.
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, Hierarchical text-conditional image generation with clip latents 10.48550/ARXIV.2204.06125 (2022)
2022
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, High-resolution image synthesis with latent diffusion models, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022) pp. 10674–10685
2022
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S. T. Brown, P. Buitrago, E. Hanna, S. Sanielevici, R. Scibek, and N. A. Nystrom, Bridges-2: A platform for rapidly-evolving and data intensive research, in Practice and Experience in Advanced Research Computing , PEARC ’21 (Association for Computing Machinery, New York, NY, USA, 2021)
2021
Cited alongside, same era.
P. Kumar, S. Kabra, and J. Cole, Auto-generating databases of yield strength and grain size using chemdataextractor, Sci Data 9
2022
Cited alongside, same era.
O. Sierepeklis and J. Cole, A thermoelectric materials database auto-generated from the scientific literature using chemdataextractor, Sci Data 9
2022
Cited alongside, same era.
E. Beard and J. Cole, Perovskite- and dye-sensitized solar-cell device databases auto-generated using chemdataextractor, Sci Data 9
2022
Cited alongside, same era.
Q. Dong and J. Cole, Auto-generated database of semiconductor band gaps using chemdataextractor, Sci Data 9
2022
Cited alongside, same era.
H. Huo, C. J. Bartel, T. He, A. Trewartha, A. Dunn, B. Ouyang, A. Jain, and G. Ceder, Machine-learning rationalization and prediction of solid-state synthesis conditions, Chemistry of Materials 34
2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe, Training language models to follow instructions with human feedback 10.48550/ARXIV.2203.02155 (2022)
2022
Cited alongside, same era.
B. Workshop, :, T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. S. Luccioni, F. Yvon, and M. Gallé et al., Bloom: A 176b-parameter open-access multilingual language model 10.48550/ARXIV.2211.05100 (2022)
2022
Cited alongside, same era.
Later among the works it cites.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, Large language models are zero-shot reasoners https://doi.org/10.48550/arXiv.2205.11916 (2022)
2022
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B. T. Afflerbach, C. Francis, L. E. Schultz, J. Spethson, V. Meschke, E. Strand, L. Ward, J. H. Perepezko, D. Thoma, P. M. Voyles, I. Szlufarska, and D. Morgan, Machine learning prediction of the critical cooling rate for metallic glasses from expanded datasets and elemental features, Chemistry of Materials 34
2022
Later among the works it cites.
L. P. J. Gilligan, M. Cobelli, V. Taufour, and S. Sanvito, A rule-free workflow for the automated generation of databases from scientific literature (2023)
2023
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M. P. Polak, S. Modi, A. Latosinska, J. Zhang, C.-W. Wang, S. Wang, A. D. Hazra, and D. Morgan, Flexible, model-agnostic method for materials data extraction from text using general purpose language models https://doi.org/10.48550/arXiv.2302.04914 (2023)
2023
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Midjourney, https://www.midjourney.com , [Online; accessed 08-Feb-2023]
2023
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2023
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2023
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facebookresearch, Llama: Inference code for llama models, https://github.com/facebookresearch/llama (2023)
2023
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