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Advancements in machine learning and artificial intelligence are transforming materials discovery.
The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime Carbonell and Jade Goldstein · 1998
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The american mineralogist crystal structure database
Robert T Downs and Michelle Hall-Wallace · 2003
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Crystallography open database–an open-access collection of crystal structures
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Crystallography open database (cod): an open-access collection of crystal structures and platform for world-wide collaboration
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Systematic modulation and enhancement of co 2: N 2 selectivity and water stability in an isoreticular series of bio-mof-11 analogues
Tao Li, De-Li Chen, Jeanne E Sullivan, Mark T Kozlowski, J Karl Johnson, and Nathaniel L Rosi · 2013
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
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Water stability and adsorption in metal–organic frameworks
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Computing stoichiometric molecular composition from crystal structures
Saulius Gražulis, Andrius Merkys, Antanas Vaitkus, and Mykolas Okulič-Kazarinas · 2015
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Chemdataextractor: a toolkit for automated extraction of chemical information from the scientific literature
Matthew C Swain and Jacqueline M Cole · 2016
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Cod:: Cif:: Parser: an error-correcting cif parser for the perl language
Andrius Merkys, Antanas Vaitkus, Justas Butkus, Mykolas Okulič-Kazarinas, Visvaldas Kairys, and Saulius Gražulis · 2016
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The cambridge structural database
Colin R Groom, Ian J Bruno, Matthew P Lightfoot, and Suzanna C Ward · 2016
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Materials synthesis insights from scientific literature via text extraction and machine learning
Edward Kim, Kevin Huang, Adam Saunders, Andrew McCallum, Gerbrand Ceder, and Elsa Olivetti · 2017
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Machine learning for molecular and materials science
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik · 2018
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Using smiles strings for the description of chemical connectivity in the crystallography open database
Miguel Quirós, Saulius Gražulis, Saulė Girdzijauskaitė, Andrius Merkys, and Antanas Vaitkus · 2018
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Named entity recognition and normalization applied to large-scale information extraction from the materials science literature
Leigh Weston, Vahe Tshitoyan, John Dagdelen, Olga Kononova, Amalie Trewartha, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2019
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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The role of machine learning in the understanding and design of materials
Seyed Mohamad Moosavi, Kevin Maik Jablonka, and Berend Smit · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Using machine learning and data mining to leverage community knowledge for the engineering of stable metal–organic frameworks
Aditya Nandy, Chenru Duan, and Heather J Kulik · 2021
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Webgpt: Browser-assisted question-answering with human feedback
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Digimof: A database of metal–organic framework synthesis information generated via text mining
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Assessment of chemistry knowledge in large language models that generate code
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Chemcrow: Augmenting large-language models with chemistry tools
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Emergent autonomous scientific research capabilities of large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Validation of the crystallography open database using the crystallographic information framework
Antanas Vaitkus, Andrius Merkys, and Saulius Gražulis · 2021
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Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Andrew S Rosen, Shaelyn M Iyer, Debmalya Ray, Zhenpeng Yao, Alan Aspuru-Guzik, Laura Gagliardi, Justin M Notestein, and Randall Q Snurr · 2021
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Structured information extraction from complex scientific text with fine-tuned large language models
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Batterybert: A pretrained language model for battery database enhancement
Shu Huang and Jacqueline M Cole · 2022
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Large language models are zero-shot reasoners
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React: Synergizing reasoning and acting in language models
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Langchain, 10 2022
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Gpt-4 technical report, 2023
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Tool learning with foundation models
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Chain-of-verification reduces hallucination in large language models
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Graph isomorphism-based algorithm for cross-checking chemical and crystallographic descriptions
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