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Leveraging large language models for universal scientific formula and theory discovery. arXiv:2503.06512. 2025."},{"key":"ref51","doi-asserted-by":"crossref","first-page":"e202418074","DOI":"10.1002\/anie.202418074","article-title":"Integrating machine learning and large language models to advance exploration of electrochemical reactions","volume":"64","author":"Zheng","year":"2025","journal-title":"Angew Chem Int Ed"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"12200","DOI":"10.1039\/D3SC07012C","article-title":"Automation and machine learning augmented by large language models in a catalysis study","volume":"15","author":"Su","year":"2024","journal-title":"Chem Sci"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1021\/acsphyschemau.4c00004","article-title":"The potential of neural network potentials","volume":"4","author":"Duignan","year":"2024","journal-title":"ACS Phys Chem Au"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1038\/s41597-024-03180-9","volume":"11","author":"Chen","year":"2024","journal-title":"Sci Data"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1021\/acs.jcim.9b00470","article-title":"Named entity recognition and normalization applied to large-scale information extraction from the materials science literature","volume":"59","author":"Weston","year":"2019","journal-title":"J Chem Inf Model"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/s41597-023-02089-z","volume":"10","author":"Wang","year":"2023","journal-title":"Sci Data"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"9436","DOI":"10.1021\/acs.chemmater.7b03500","article-title":"Materials synthesis insights from scientific literature via text extraction and machine learning","volume":"29","author":"Kim","year":"2017","journal-title":"Chem Mater"},{"key":"ref58","unstructured":"PEESEgroup\/Awesome-materials-aware-large-language-models [Internet]. [cited 2025 Jun 11]. Available from: https:\/\/github.com\/PEESEgroup\/Awesome-Materials-Aware-Large-Language-Models."},{"key":"ref59","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1021\/acs.jcim.9b00995","article-title":"Inorganic materials synthesis planning with literature-trained neural networks","volume":"60","author":"Kim","year":"2020","journal-title":"J Chem Inf Model"},{"key":"ref60","first-page":"1","article-title":"Accurate, interpretable predictions of materials properties within machine-learned interatomic potentials using explainable graph neural networks","volume":"9","author":"Zuo","year":"2023","journal-title":"npj Comput Mater"},{"key":"ref61","doi-asserted-by":"crossref","first-page":"16032","DOI":"10.1021\/acscatal.3c04956","article-title":"Catalyst energy prediction with CatBERTa: unveiling feature exploration strategies through large language models","volume":"13","author":"Ock","year":"2023","journal-title":"ACS Catal"},{"key":"ref62","unstructured":"Shoghi N, Kolluru A, Kitchin JR, Ulissi ZW, Zitnick CL, Wood BM. From molecules to materials: pre-training large generalizable models for atomic property prediction. arXiv:2310.1680. 2023."},{"key":"ref63","first-page":"14849","article-title":"Language models for materials discovery and sustainability: progress, challenges, and opportunities","volume":"145","author":"Pei","year":"2025","journal-title":"Prog Mater Sci"},{"key":"ref64","unstructured":"Mok DH, Back S. Language model-based generative model for catalyst discovery Paper presented at: 2024 MRS Fall Meeting & Exhibit; 2024; Boston, MA, USA: Materials Research Society [Intermet]. [cited 2025 Jun 1]. Available from: https:\/\/www.mrs.org\/meetings-events\/annual-meetings\/archive\/meeting\/presentations\/view\/2024-fall-meeting\/2024-fall-meeting-4148755."},{"key":"ref65","doi-asserted-by":"crossref","first-page":"33712","DOI":"10.1021\/jacs.4c11504","article-title":"Generative pretrained transformer for heterogeneous catalysts","volume":"146","author":"Mok","year":"2024","journal-title":"J Am Chem Soc"},{"key":"ref66","doi-asserted-by":"crossref","first-page":"115906","DOI":"10.1016\/j.jcat.2024.115906","article-title":"Leveraging Machine learning and active motifs-based catalyst design for discovery of oxygen reduction electrocatalysts for hydrogen peroxide production","volume":"442","author":"Yu","year":"2025","journal-title":"J Catal"},{"key":"ref67","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1039\/D4SC04757E","article-title":"SynAsk: unleashing the power of large language models in organic synthesis","volume":"16","author":"Zhang","year":"2025","journal-title":"Chem Sci"},{"key":"ref68","doi-asserted-by":"crossref","first-page":"2748","DOI":"10.1021\/acs.jcim.4c01529","article-title":"LLM-driven synthesis planning for quantum dot materials development","volume":"65","author":"Choi","year":"2025","journal-title":"J Chem Inf Model"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"121567","DOI":"10.1016\/j.ces.2025.121567","article-title":"Chat-microreactor: a large-language-model-based assistant for designing continuous flow systems","volume":"311","author":"Pan","year":"2025","journal-title":"Chem Eng Sci"},{"key":"ref70","doi-asserted-by":"crossref","first-page":"4411","DOI":"10.1021\/acssuschemeng.3c06920","volume":"12","author":"Bandeira","year":"2024","journal-title":"ACS Sustain Chem Eng"},{"key":"ref71","unstructured":"Zitnick CL, Chanussot L, Das A, Goyal S, Heras-Domingo J, Ho C, et al. 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