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Large language models (LLMs) are increasingly being used in materials science.
The crystallographic information file (cif): a new standard archive file for crystallography
Hall, S. R., Allen, F. H., and Brown, I. D. (1991) · 1991
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Large-scale screening of hypothetical metal–organic frameworks
Wilmer, C. E., Leaf, M., Lee, C. Y., Farha, O. K., Hauser, B. G., Hupp, J. T., and Snurr, R. Q. (2012) · 2012
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The materials project: A materials genome approach to accelerating materials innovation. apl materials, 1 (1): 011002, 2013
Jain, A., Ong, S., Hautier, G., Chen, W., Richards, W., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · 2013
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The open quantum materials database (oqmd): assessing the accuracy of dft formation energies
Kirklin, S., Saal, J. E., Meredig, B., Thompson, A., Doak, J. W., Aykol, M., Rühl, S., and Wolverton, C. (2015) · 2015
Earlier work this paper cites.
Organic materials database: An open-access online database for data mining
Borysov, S. S., Geilhufe, R. M., and Balatsky, A. V. (2017) · 2017
Earlier work this paper cites.
High-throughput identification and characterization of two-dimensional materials using density functional theory
Choudhary, K., Kalish, I., Beams, R., and Tavazza, F. (2017) · 2017
Earlier work this paper cites.
Computational screening of high-performance optoelectronic materials using optb88vdw and tb-mbj formalisms
Choudhary, K., Zhang, Q., Reid, A. C., Chowdhury, S., Van Nguyen, N., Trautt, Z., Newrock, M. W., Congo, F. Y., and Tavazza, F. (2018) · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018) · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al. (2018) · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie, T. and Grossman, J. C. (2018) · 2018
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Super-convergence: Very fast training of neural networks using large learning rates
Smith, L. N. and Topin, N. (2019) · 2019
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Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
Dunn, A., Wang, Q., Ganose, A., Dopp, D., and Jain, A. (2020) · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2020) · 2020
Earlier work this paper cites.
Atomistic line graph neural network for improved materials property predictions
Choudhary, K. and DeCost, B. (2021) · 2021
Earlier work this paper cites.
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Rosen, A. S., Iyer, S. M., Ray, D., Yao, Z., Aspuru-Guzik, A., Gagliardi, L., Notestein, J. M., and Snurr, R. Q. (2021) · 2021
Earlier work this paper cites.
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) · 2021
Cited alongside, same era.
Translation between molecules and natural language
Edwards, C., Lai, T., Ros, K., Honke, G., Cho, K., and Ji, H. (2022) · 2022
Cited alongside, same era.
Language models of protein sequences at the scale of evolution enable accurate structure prediction
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al. (2022) · 2022
Cited alongside, same era.
Scalable deeper graph neural networks for high-performance materials property prediction
Omee, S. S., Louis, S.-Y., Fu, N., Wei, L., Dey, S., Dong, R., Li, Q., and Hu, J. (2022) · 2022
Cited alongside, same era.
High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration
Rosen, A. S., Fung, V., Huck, P., O’Donnell, C. T., Horton, M. K., Truhlar, D. G., Persson, K. A., Notestein, J. M., and Snurr, R. Q. (2022) · 2022
Fine-tuned language models generate stable inorganic materials as text
Gruver, N., Sriram, A., Madotto, A., Wilson, A. G., Zitnick, C. L., and Ulissi, Z. W. (2023) · 2023
Later among the works it cites.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al. (2023) · 2023
Later among the works it cites.
Accurate, interpretable predictions of materials properties within transformer language models
Korolev, V. and Protsenko, P. (2023) · 2023
Later among the works it cites.
Scaling deep learning for materials discovery
Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., and Cubuk, E. D. (2023) · 2023
Later among the works it cites.
Rubungo, A. N., Arnold, C., Rand, B. P., and Dieng, A. B. (2023) · 2023
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Cited alongside, same era.
What information is necessary and sufficient to predict materials properties using machine learning?
Tian, S. I. P., Walsh, A., Ren, Z., Li, Q., and Buonassisi, T. (2022) · 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) · 2023
Cited alongside, same era.
Crystal structure generation with autoregressive large language modeling
Antunes, L. M., Butler, K. T., and Grau-Crespo, R. (2023) · 2023
Cited alongside, same era.
Do large language models understand chemistry? a conversation with chatgpt
Castro Nascimento, C. M. and Pimentel, A. S. (2023) · 2023
Cited alongside, same era.
Crysmmnet: multimodal representation for crystal property prediction
Das, K., Goyal, P., Lee, S.-C., Bhattacharjee, S., and Ganguly, N. (2023) · 2023
Cited alongside, same era.
Mol-instructions-a large-scale biomolecular instruction dataset for large language models
Fang, Y., Liang, X., Zhang, N., Liu, K., Huang, R., Chen, Z., Fan, X., and Chen, H. (2023) · 2023
Cited alongside, same era.
Flam-Shepherd, D. and Aspuru-Guzik, A. (2023) · 2023
Cited alongside, same era.
Later among the works it cites.
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) · 2023
Later among the works it cites.
The promises of large language models for protein design and modeling
Valentini, G., Malchiodi, D., Gliozzo, J., Mesiti, M., Soto-Gomez, M., Cabri, A., Reese, J., Casiraghi, E., and Robinson, P. N. (2023) · 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) · 2023
Later among the works it cites.
Chiang, Y., Chou, C.-H., and Riebesell, J. (2024) · 2024
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Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design
Choudhary, K. (2024) · 2024
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Jarvis-leaderboard: a large scale benchmark of materials design methods
Choudhary, K., Wines, D., Li, K., Garrity, K. F., Gupta, V., Romero, A. H., Krogel, J. T., Saritas, K., Fuhr, A., Ganesh, P., et al. (2024) · 2024
Closest in time.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al. (2024) · 2024
Closest in time.
Prollama: A protein large language model for multi-task protein language processing
Lv, L., Lin, Z., Li, H., Liu, Y., Cui, J., Chen, C. Y.-C., Yuan, L., and Tian, Y. (2024) · 2024
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Leveraging language representation for materials exploration and discovery
Qu, J., Xie, Y. R., Ciesielski, K. M., Porter, C. E., Toberer, E. S., and Ertekin, E. (2024) · 2024
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