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Nonequilibrium phase diagrams of ternary amorphous alloys, 1997
Yoshiyuki Kawazoe, J-Z Yu, A-P Tsai, and T Masumoto · 1997
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ESOL: estimating aqueous solubility directly from molecular structure
John S Delaney · 2004
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ChEMBL: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, and Bissan Al-Lazikani · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Elementary school science and math tests as a driver for ai: Take the aristo challenge!
Peter Clark · 2015
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Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F Liu, and Matt Gardner · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Predicting the band gaps of inorganic solids by machine learning
Ya Zhuo, Aria Mansouri Tehrani, and Jakoah Brgoch · 2018
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Computer-aided screening of conjugated polymers for organic solar cell: classification by random forest
Shinji Nagasawa, Eman Al-Naamani, and Akinori Saeki · 2018
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Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature
L Weston, V Tshitoyan, J Dagdelen, O Kononova, A Trewartha, K A Persson, G Ceder, and A Jain · 2019
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Scibert: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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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, and Amanda Askell · 2020
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Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain · 2020
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Discovering rare-earth-free magnetic materials through the development of a database
Masahiro Sakurai, Renhai Wang, Timothy Liao, Chao Zhang, Huaijun Sun, Yang Sun, Haidi Wang, Xin Zhao, Songyou Wang, and Balamurugan Balasubramanian · 2020
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Understanding the diversity of the metal-organic framework ecosystem
Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, Daniele Ongari, Jon Paul Janet, Peter G Boyd, Yongjin Lee, Berend Smit, and Heather J Kulik · 2020
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Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules
Zongrui Pei, Junqi Yin, Jeffrey A Hawk, David E Alman, and Michael C Gao · 2020
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Prediction of water stability of metal–organic frameworks using machine learning
Rohit Batra, Carmen Chen, Tania G Evans, Krista S Walton, and Rampi Ramprasad · 2020
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
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Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome
Yanrong Ji, Zhihan Zhou, Han Liu, and Ramana V Davuluri · 2021
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HybriD 3: Materials Property Database for Hybrid Organic-Inorganic Perovskites
Xixi Qin, Xiaochen Du, Sampreeti Bhattacharya, Connor Clayton, Jun Hu, Manoj Jana, Svenja Janke, Rebecca Lau, Raul Laasner, and Andrew Levin · 2021
Large language models as master key: Unlocking the secrets of materials science with gpt, 2023
Tong Xie, Yuwei Wan, Wei Huang, Yufei Zhou, Yixuan Liu, Qingyuan Linghu, Shaozhou Wang, Chunyu Kit, Clara Grazian, Wenjie Zhang, and Bram Hoex · 2023
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Is GPT-3 all you need for low-data discovery in chemistry ?
Kevin Maik Jablonka, Philippe Schwaller, and Andres Ortega-guerrero · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, and Faisal Azhar · 2023
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RWKV: Reinventing RNNs for the Transformer Era
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, and Kranthi Kiran GV · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science
Amalie Trewartha, Nicholas Walker, Haoyan Huo, Sanghoon Lee, Kevin Cruse, John Dagdelen, Alexander Dunn, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, and Alex Ray · 2022
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Self-Instruct: Aligning Language Model with Self Generated Instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
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Benchmarking generalization via in-context instructions on 1,600+ language tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, et al · 2022
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Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, et al · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
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Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science
Amalie Trewartha, Nicholas Walker, Haoyan Huo, Sanghoon Lee, Kevin Cruse, John Dagdelen, Alexander Dunn, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2022
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Huatuo: Tuning llama model with chinese medical knowledge
Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang, Sendong Zhao, Bing Qin, and Ting Liu · 2023
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The curse of recursion: Training on generated data makes models forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson · 2023
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Pmc-llama: Further finetuning llama on medical papers
Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie · 2023
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Chemcrow: Augmenting large-language models with chemistry tools
Andres M Bran, Sam Cox, Andrew D White, and Philippe Schwaller · 2023
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Mol-instructions: A large-scale biomolecular instruction dataset for large language models
Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan, and Huajun Chen · 2023
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Is gpt-3 all you need for low-data discovery in chemistry?
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit · 2023
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Device performance of emerging photovoltaic materials (Version 3)
Osbel Almora, Derya Baran, Guillermo C Bazan, Carlos I Cabrera, Sule Erten-Ela, Karen Forberich, Fei Guo, Jens Hauch, Anita W Y Ho-Baillie, and T Jesper Jacobsson · 2023
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Polar metals taxonomy for materials classification and discovery
Daniel Hickox-Young, Danilo Puggioni, and James M Rondinelli · 2023
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