Natl Sci Open
Volume 3, Number 2, 2024
Special Topic: AI for Chemistry
Article Number 20230040
Number of page(s) 10
Section Chemistry
Published online 06 February 2024
  • Butler KT, Davies DW, Cartwright H, et al. Machine learning for molecular and materials science. Nature 2018; 559: 547-555. [Article] [Google Scholar]
  • de Almeida AF, Moreira R, Rodrigues T. Synthetic organic chemistry driven by artificial intelligence. Nat Rev Chem 2019; 3: 589-604. [Article] [Google Scholar]
  • Gomes CP, Selman B, Gregoire JM. Artificial intelligence for materials discovery. MRS Bull 2019; 44: 538-544. [Article] [Google Scholar]
  • Pei Z, Yin J, Liaw PK, et al. Toward the design of ultrahigh-entropy alloys via mining six million texts. Nat Commun 2023; 14: 54. [Article] [Google Scholar]
  • Kononova O, Huo H, He T, et al. Text-mined dataset of inorganic materials synthesis recipes. Sci Data 2019; 6: 203. [Article] [Google Scholar]
  • He T, Sun W, Huo H, et al. Similarity of precursors in solid-state synthesis as text-mined from scientific literature. Chem Mater 2020; 32: 7861-7873. [Article] [Google Scholar]
  • Kumar A, Ganesh S, Gupta D, et al. A text mining framework for screening catalysts and critical process parameters from scientific literature—A study on hydrogen production from alcohol. Chem Eng Res Des 2022; 184: 90-102. [Article] [Google Scholar]
  • Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI Conference on Artificial Intelligence. Austin, 2015 [Google Scholar]
  • Pujara J, Miao H, Getoor L, et al. Knowledge graph identification. In: International Semantic Web Conference. Athens, 2013, 542-557 [Google Scholar]
  • Wang Q, Mao Z, Wang B, et al. Knowledge graph embedding: A survey of approaches and applications. IEEE Trans Knowl Data Eng 2017; 29: 2724-2743. [Article] [Google Scholar]
  • Nie Z, Liu Y, Yang L, et al. Construction and application of materials knowledge graph based on author disambiguation: Revisiting the evolution of LiFePO4. Adv Energy Mater 2021; 11: 2003580. [Article] [Google Scholar]
  • Rindflesch TC, Fiszman M. The interaction of domain knowledge and linguistic structure in natural language processing: Interpreting hypernymic propositions in biomedical text. J BioMed Inf 2003; 36: 462-477. [Article] [Google Scholar]
  • Rindflesch TC, Kilicoglu H, Fiszman M, et al. Semantic MEDLINE: An advanced information management application for biomedicine. Inform Serv Use 2011; 31: 15-21. [Article] [Google Scholar]
  • Gu Y, Tinn R, Cheng H, et al. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans Comput Healthc 2022; 3: 1-23. [Article] [Google Scholar]
  • Hong L, Lin J, Li S, et al. A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories. Nat Mach Intell 2020; 2: 347-355. [Article] [Google Scholar]
  • Manica M, Mathis R, Cadow J, et al. Context-specific interaction networks from vector representation of words. Nat Mach Intell 2019; 1: 181-190. [Article] [Google Scholar]
  • Harnoune A, Rhanoui M, Mikram M, et al. BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis. Comput Methods Programs Biomed Update 2021; 1: 100042. [Article] [Google Scholar]
  • Nicholson DN, Greene CS. Constructing knowledge graphs and their biomedical applications. Comput Struct Biotechnol J 2020; 18: 1414-1428. [Article] [Google Scholar]
  • Santos A, Colaço AR, Nielsen AB, et al. A knowledge graph to interpret clinical proteomics data. Nat Biotechnol 2022; 40: 692-702. [Article] [Google Scholar]
  • Wang X, Meng L, Wang X, et al. The construction of environmental-policy-enterprise knowledge graph based on PTA model and PSA model. Resour Conserv Recycl Adv 2021; 12: 200057. [Article] [Google Scholar]
  • Mrdjenovich D, Horton MK, Montoya JH, et al. Propnet: A knowledge graph for materials science. Matter 2020; 2: 464-480. [Article] [Google Scholar]
  • Nie Z, Zheng S, Liu Y, et al. Automating materials exploration with a semantic knowledge graph for Li-ion battery cathodes. Adv Funct Mater 2022; 32: 2201437. [Article] [Google Scholar]
  • Aramouni NAK, Touma JG, Tarboush BA, et al. Catalyst design for dry reforming of methane: Analysis review. Renew Sustain Energy Rev 2018; 82: 2570-2585. [Article] [Google Scholar]
  • Guo W, Zhang K, Liang Z, et al. Electrochemical nitrogen fixation and utilization: Theories, advanced catalyst materials and system design. Chem Soc Rev 2019; 48: 5658-5716. [Article] [Google Scholar]
  • Abdulrasheed A, Jalil AA, Gambo Y, et al. A review on catalyst development for dry reforming of methane to syngas: Recent advances. Renew Sustain Energy Rev 2019; 108: 175-193. [Article] [Google Scholar]
  • Garg S, Li M, Weber AZ, et al. Advances and challenges in electrochemical CO2 reduction processes: an engineering and design perspective looking beyond new catalyst materials. J Mater Chem A 2020; 8: 1511-1544. [Article] [Google Scholar]
  • Feng X, Liu H, He C, et al. Synergistic effects and mechanism of a non-thermal plasma catalysis system in volatile organic compound removal: A review. Catal Sci Technol 2018; 8: 936-954. [Article] [Google Scholar]
  • Winther KT, Hoffmann MJ, Boes JR, et al., an open electronic structure database for surface reactions. Sci Data 2019; 6: 75. [Article] [Google Scholar]
  • Shanghai Institute of Organic Chemistry of CAS. Chemistry Database [1978–2023]. [Google Scholar]
  • Devlin J, Chang MW, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arxiv:1810.04805, 2018 [Google Scholar]
  • Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res 2008; 9: 2579-2605 [Google Scholar]

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