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Unsupervised word embeddings capture latent knowledge from materials science literature

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Cited by:

  1. Jia-Min Lu & Hui-Feng Wang & Qi-Hang Guo & Jian-Wei Wang & Tong-Tong Li & Ke-Xin Chen & Meng-Ting Zhang & Jian-Bo Chen & Qian-Nuan Shi & Yi Huang & Shao-Wen Shi & Guang-Yong Chen & Jian-Zhang Pan & Zh, 2024. "Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  2. Sadamori Kojaku & Filippo Radicchi & Yong-Yeol Ahn & Santo Fortunato, 2024. "Network community detection via neural embeddings," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  3. Ananthan Nambiar & Tobias Rubel & James McCaull & Jon deVries & Mark Bedau, 2021. "Dropping diversity of products of large US firms: Models and measures," Papers 2110.08367, arXiv.org.
  4. Sotaro Shibayama & Deyun Yin & Kuniko Matsumoto, 2021. "Measuring novelty in science with word embedding," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-16, July.
  5. Lin, Yiling & Evans, James A. & Wu, Lingfei, 2022. "New directions in science emerge from disconnection and discord," Journal of Informetrics, Elsevier, vol. 16(1).
  6. Mauricio Toledo-Acosta & Talin Barreiro & Asela Reig-Alamillo & Markus Müller & Fuensanta Aroca Bisquert & Maria Luisa Barrigon & Enrique Baca-Garcia & Jorge Hermosillo-Valadez, 2020. "Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
  7. Martín de Diego, Isaac & González-Fernández, César & Fernández-Isabel, Alberto & Fernández, Rubén R. & Cabezas, Javier, 2021. "System for evaluating the reliability and novelty of medical scientific papers," Journal of Informetrics, Elsevier, vol. 15(4).
  8. Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  9. Yonghe Lu & Jiayi Luo & Ying Xiao & Hou Zhu, 2021. "Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6937-6963, August.
  10. Aman Kumar & Binil Starly, 2022. "“FabNER”: information extraction from manufacturing process science domain literature using named entity recognition," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2393-2407, December.
  11. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
  12. Zongrui Pei & Junqi Yin & Peter K. Liaw & Dierk Raabe, 2023. "Toward the design of ultrahigh-entropy alloys via mining six million texts," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  13. Jarrahi, Mohammad Hossein & Askay, David & Eshraghi, Ali & Smith, Preston, 2023. "Artificial intelligence and knowledge management: A partnership between human and AI," Business Horizons, Elsevier, vol. 66(1), pages 87-99.
  14. Wu, Lingfei & Kittur, Aniket & Youn, Hyejin & Milojević, Staša & Leahey, Erin & Fiore, Stephen M. & Ahn, Yong-Yeol, 2022. "Metrics and mechanisms: Measuring the unmeasurable in the science of science," Journal of Informetrics, Elsevier, vol. 16(2).
  15. Yan Duan & Lorena E. Rosaleny & Joana T. Coutinho & Silvia Giménez-Santamarina & Allen Scheie & José J. Baldoví & Salvador Cardona-Serra & Alejandro Gaita-Ariño, 2022. "Data-driven design of molecular nanomagnets," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  16. Gordana Ispirova & Tome Eftimov & Barbara Koroušić Seljak, 2020. "P-NUT: Predicting NUTrient Content from Short Text Descriptions," Mathematics, MDPI, vol. 8(10), pages 1-21, October.
  17. Shaoshuo Li & Baixing Chen & Hao Chen & Zhen Hua & Yang Shao & Heng Yin & Jianwei Wang, 2021. "Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-18, September.
  18. David Chavalarias & Quentin Lobbé & Alexandre Delanoë, 2022. "Draw me Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 545-575, January.
  19. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).
  20. Shufeng Kong & Francesco Ricci & Dan Guevarra & Jeffrey B. Neaton & Carla P. Gomes & John M. Gregoire, 2022. "Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  21. Jiang, Zhuoren & Lin, Tianqianjin & Huang, Cui, 2023. "Deep representation learning of scientific paper reveals its potential scholarly impact," Journal of Informetrics, Elsevier, vol. 17(1).
  22. Mojan Omidvar & Hangfeng Zhang & Achintha Avin Ihalage & Theo Graves Saunders & Henry Giddens & Michael Forrester & Sajad Haq & Yang Hao, 2024. "Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  23. Zachary A Pardos & Andrew Joo Hun Nam, 2020. "A university map of course knowledge," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-24, September.
  24. Jianhong Luo & Minjuan Chai & Xuwei Pan, 2021. "Identification of Research Priorities during the COVID-19 Pandemic: Implications for Its Management," IJERPH, MDPI, vol. 18(24), pages 1-15, December.
  25. Pessa, Arthur A.B. & Zola, Rafael S. & Perc, Matjaž & Ribeiro, Haroldo V., 2022. "Determining liquid crystal properties with ordinal networks and machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
  26. Lucie Beranová & Marcin P. Joachimiak & Tomáš Kliegr & Gollam Rabby & Vilém Sklenák, 2022. "Why was this cited? Explainable machine learning applied to COVID-19 research literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2313-2349, May.
  27. John Dagdelen & Alexander Dunn & Sanghoon Lee & Nicholas Walker & Andrew S. Rosen & Gerbrand Ceder & Kristin A. Persson & Anubhav Jain, 2024. "Structured information extraction from scientific text with large language models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  28. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
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