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Large Language Models (LLMs) create exciting possibilities for powerful language processing tools to accelerate research in materials science.
Bilbao crystallographic server: I. databases and crystallographic computing programs
Aroyo, M. I., Perez-Mato, J. M., Capillas, C., Kroumova, E., Ivantchev, S., Madariaga, G., Kirov, A., and Wondratschek, H · 2006
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Indentation across size scales and disciplines: Recent developments in experimentation and modeling
Gouldstone, A., Chollacoop, N., Dao, M., Li, J., Minor, A. M., and Shen, Y.-L · 2007
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The cambridge structural database
Groom, C. R., Bruno, I. J., Lightfoot, M. P., and Ward, S. C · 2016
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Chemdataextractor: a toolkit for automated extraction of chemical information from the scientific literature
Swain, M. C. and Cole, J. M · 2016
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Materials synthesis insights from scientific literature via text extraction and machine learning
Kim, E., Huang, K., Saunders, A., McCallum, A., Ceder, G., and Olivetti, E · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Robocrystallographer: automated crystal structure text descriptions and analysis
Ganose, A. M. and Jain, A · 2019
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Imagedataextractor: a tool to extract and quantify data from microscopy images
Mukaddem, K. T., Beard, E. J., Yildirim, B., and Cole, J. M · 2019
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Mysore, S., Jensen, Z., Kim, E., Huang, K., Chang, H.-S., Strubell, E., Flanigan, J., McCallum, A., and Olivetti, E · 2019
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Brief guide to the nomenclature of organic chemistry (iupac technical report)
Hellwich, K.-H., Hartshorn, R. M., Yerin, A., Damhus, T., and Hutton, A. T · 2020
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Inorganic materials synthesis planning with literature-trained neural networks
Kim, E., Jensen, Z., van Grootel, A., Huang, K., Staib, M., Mysore, S., Chang, H.-S., Strubell, E., McCallum, A., Jegelka, S., et al · 2020
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S2ORC: The semantic scholar open research corpus
Lo, K., Wang, L. L., Neumann, M., Kinney, R., and Weld, D · 2020
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Data-driven materials research enabled by natural language processing and information extraction
Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., and Hiszpanski, A. M · 2020
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
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Discovering relationships between osdas and zeolites through data mining and generative neural networks
Jensen, Z., Kwon, S., Schwalbe-Koda, D., Paris, C., Gómez-Bombarelli, R., Román-Leshkov, Y., Corma, A., Moliner, M., and Olivetti, E. A · 2021
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Towards understanding the scratchability in functional glasses
Kasimuthumaniyan, S., Gosvami, N. N., and Krishnan, N. A · 2021
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The open reaction database
Kearnes, S. M., Maser, M. R., Wleklinski, M., Kast, A., Doyle, A. G., Dreher, S. D., Hawkins, J. M., Jensen, K. F., and Coley, C. W · 2021
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Opportunities and challenges of text mining in materials research
Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., and Ceder, G · 2021
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Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century
Ravinder, Venugopal, V., Bishnoi, S., Singh, S., Zaki, M., Grover, H. S., Bauchy, M., Agarwal, M., and Krishnan, N. A · 2021
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Looking through glass: Knowledge discovery from materials science literature using natural language processing
Venugopal, V., Sahoo, S., Zaki, M., Agarwal, M., Gosvami, N. N., and Krishnan, N. A · 2021
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The impact of domain-specific pre-training on named entity recognition tasks in materials science
Walker, N., Trewartha, A., Huo, H., Lee, S., Cruse, K., Dagdelen, J., Dunn, A., Persson, K., Ceder, G., and Jain, A · 2021
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Crystal diffusion variational autoencoder for periodic material generation
Xie, T., Fu, X., Ganea, O.-E., Barzilay, R., and Jaakkola, T · 2021
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Matscibert: A materials domain language model for text mining and information extraction
Gupta, T., Zaki, M., Krishnan, N. A., and Mausam · 2022
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Learning inverse folding from millions of predicted structures
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., and Rives, A · 2022
Earlier work this paper cites.
Batterybert: A pretrained language model for battery database enhancement
Huang, S. and Cole, J. M · 2022
Earlier work this paper cites.
Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model
Vasudevan, R. K., Orozco, E., and Kalinin, S. V · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Cited alongside, same era.
A database of refractive indices and dielectric constants auto-generated using chemdataextractor
Zhao, J. and Cole, J. M · 2022
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Cited alongside, same era.
Crystal structure generation with autoregressive large language modeling
Antunes, L. M., Butler, K. T., and Grau-Crespo, R · 2023
Data sharing in chemistry: lessons learned and a case for mandating structured reaction data
Mercado, R., Kearnes, S. M., and Coley, C. W · 2023
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Scaling deep learning for materials discovery
Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., and Cubuk, E. D · 2023
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Rubungo, A. N., Arnold, C., Rand, B. P., and Dieng, A. B · 2023
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Chemos 2.0: an orchestration architecture for chemical self-driving laboratories
Sim, M., Vakili, M. G., Strieth-Kalthoff, F., Hao, H., Hickman, R., Miret, S., Pablo-García, S., and Aspuru-Guzik, A · 2023
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MatSci-NLP: Evaluating scientific language models on materials science language tasks using text-to-schema modeling
Song, Y., Miret, S., and Liu, B · 2023
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Cited alongside, same era.
Can retriever-augmented language models reason? the blame game between the retriever and the language model
BehnamGhader, P., Miret, S., and Reddy, S · 2023
Cited alongside, same era.
Autonomous chemical research with large language models
Boiko, D. A., MacKnight, R., Kline, B., and Gomes, G · 2023
Cited alongside, same era.
Chemcrow: Augmenting large-language models with chemistry tools
Bran, A. M., Cox, S., White, A. D., and Schwaller, P · 2023
Cited alongside, same era.
Generative retrieval-augmented ontologic graph and multiagent strategies for interpretive large language model-based materials design
Buehler, M. J · 2023
Cited alongside, same era.
Redpajama: an open dataset for training large language models, 2023
Computer, T · 2023
Cited alongside, same era.
scgpt: Towards building a foundation model for single-cell multi-omics using generative ai
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., and Wang, B · 2023
Cited alongside, same era.
The nucleotide transformer: Building and evaluating robust foundation models for human genomics
Dalla-Torre, H., Gonzalez, L., Mendoza Revilla, J., Lopez Carranza, N., Henryk Grywaczewski, A., Oteri, F., Dallago, C., Trop, E., Sirelkhatim, H., Richard, G., et al · 2023
Cited alongside, same era.
Honeybee: Progressive instruction finetuning of large language models for materials science
Song, Y., Miret, S., Zhang, H., and Liu, B · 2023
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An autonomous laboratory for the accelerated synthesis of novel materials
Szymanski, N. J., Rendy, B., Fei, Y., Kumar, R. E., He, T., Milsted, D., McDermott, M. J., Gallant, M., Cubuk, E. D., Merchant, A., et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Assessment of chemistry knowledge in large language models that generate code
White, A. D., Hocky, G. M., Gandhi, H. A., Ansari, M., Cox, S., Wellawatte, G. P., Sasmal, S., Yang, Z., Liu, K., Singh, Y., et al · 2023
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Darwin series: Domain specific large language models for natural science
Xie, T., Wan, Y., Huang, W., Yin, Z., Liu, Y., Wang, S., Linghu, Q., Kit, C., Grazian, C., Zhang, W., et al · 2023
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Protst: Multi-modality learning of protein sequences and biomedical texts
Xu, M., Yuan, X., Miret, S., and Tang, J · 2023
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Large language models for chemistry robotics
Yoshikawa, N., Skreta, M., Darvish, K., Arellano-Rubach, S., Ji, Z., Bjørn Kristensen, L., Li, A. Z., Zhao, Y., Xu, H., Kuramshin, A., et al · 2023
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Cementron: Machine learning the alite and belite phases in cement clinker from optical images
Zaki, M., Sharma, S., Gurjar, S. K., Goyal, R., Krishnan, N. A., et al · 2023
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Large language models meet nl2code: A survey
Zan, D., Chen, B., Zhang, F., Lu, D., Wu, B., Guan, B., Yongji, W., and Lou, J.-G · 2023
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Mattergen: a generative model for inorganic materials design
Zeni, C., Pinsler, R., Zügner, D., Fowler, A., Horton, M., Fu, X., Shysheya, S., Crabbé, J., Sun, L., Smith, J., et al · 2023
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Text-to-image diffusion model in generative ai: A survey
Zhang, C., Zhang, C., Zhang, M., and Kweon, I. S · 2023
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Opticalbert and opticaltable-sqa: Text-and table-based language models for the optical-materials domain
Zhao, J., Huang, S., and Cole, J. M · 2023
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Mechgpt, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines, and modalities
Buehler, M. J · 2024
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Structured information extraction from scientific text with large language models
Dagdelen, J., Dunn, A., Lee, S., Walker, N., Rosen, A. S., Ceder, G., Persson, K. A., and Jain, A · 2024
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Large legal fictions: Profiling legal hallucinations in large language models
Dahl, M., Magesh, V., Suzgun, M., and Ho, D. E · 2024
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Are large language models superhuman chemists?
Mirza, A., Alampara, N., Kunchapu, S., Emoekabu, B., Krishnan, A., Wilhelmi, M., Okereke, M., Eberhardt, J., Elahi, A. M., Greiner, M., et al · 2024
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Matkg: An autonomously generated knowledge graph in material science
Venugopal, V. and Olivetti, E · 2024
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Llms can design sustainable concrete–a systematic benchmark
Völker, C., Rug, T., Jablonka, K. M., and Kruschwitz, S · 2024
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Mascqa: Investigating materials science knowledge of large language models
Zaki, M., Jayadeva, J., Mausam, M., and Krishnan, N. A · 2024
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