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The prediction of crystal properties plays a crucial role in the crystal design process.
A high-throughput infrastructure for density functional theory calculations
Jain, A., Hautier, G., Moore, C. J., Ong, S. P., Fischer, C. C., Mueller, T., Persson, K. A., and Ceder, G. (2011) · 2011
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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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Combinatorial screening for new materials in unconstrained composition space with machine learning
Meredig, B., Agrawal, A., Kirklin, S., Saal, J. E., Doak, J. W., Thompson, A., Zhang, K., Choudhary, A., and Wolverton, C. (2014) · 2014
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Charting the complete elastic properties of inorganic crystalline compounds
De Jong, M., Chen, W., Angsten, T., Jain, A., Notestine, R., Gamst, A., Sluiter, M., Krishna Ande, C., Van Der Zwaag, S., Plata, J. J., et al. (2015) · 2015
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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
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High-throughput machine-learning-driven synthesis of full-heusler compounds
Oliynyk, A. O., Antono, E., Sparks, T. D., Ghadbeigi, L., Gaultois, M. W., Meredig, B., and Mar, A. (2016) · 2016
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Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., and Norquist, A. J. (2016) · 2016
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A general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., and Wolverton, C. (2016) · 2016
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R. (2017) · 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. (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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Scibert: A pretrained language model for scientific text
Beltagy, I., Lo, K., and Cohan, A. (2019) · 2019
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Graph networks as a universal machine learning framework for molecules and crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C., and Ong, S. P. (2019) · 2019
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Robocrystallographer: automated crystal structure text descriptions and analysis
Ganose, A. M. and Jain, A. (2019) · 2019
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Graph neural networks for predicting protein functions
Ioannidis, V. N., Marques, A. G., and Giannakis, G. B. (2019) · 2019
Cited alongside, same era.
Super-convergence: Very fast training of neural networks using large learning rates
Smith, L. N. and Topin, N. (2019) · 2019
Cited alongside, same era.
Do nlp models know numbers? probing numeracy in embeddings
Wallace, E., Wang, Y., Li, S., Singh, S., and Gardner, M. (2019) · 2019
Cited alongside, same era.
Topic modeling in embedding spaces
Dieng, A. B., Ruiz, F. J., and Blei, D. M. (2020) · 2020
Cited alongside, same era.
Injecting numerical reasoning skills into language models
Geva, M., Gupta, A., and Berant, J. (2020) · 2020
Cited alongside, same era.
Drug–target affinity prediction using graph neural network and contact maps
Jiang, M., Li, Z., Zhang, S., Wang, S., Wang, X., Yuan, Q., and Wei, Z. (2020) · 2020
Representing numbers in nlp: a survey and a vision
Thawani, A., Pujara, J., Ilievski, F., and Szekely, P. (2021) · 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) · 2021
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A universal graph deep learning interatomic potential for the periodic table
Chen, C. and Ong, S. P. (2022) · 2022
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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) · 2022
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Batterybert: A pretrained language model for battery database enhancement
Huang, S. and Cole, J. M. (2022) · 2022
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Cited alongside, same era.
Spanbert: Improving pre-training by representing and predicting spans
Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., and Levy, O. (2020) · 2020
Cited alongside, same era.
Kinnews and kirnews: Benchmarking cross-lingual text classification for kinyarwanda and kirundi
Niyongabo, R. A., Hong, Q., Kreutzer, J., and Huang, L. (2020) · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Fast and flexible protein design using deep graph neural networks
Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., and Kim, P. M. (2020) · 2020
Cited alongside, same era.
Do language embeddings capture scales?
Zhang, X., Ramachandran, D., Tenney, I., Elazar, Y., and Roth, D. (2020) · 2020
Cited alongside, same era.
Atomistic line graph neural network for improved materials property predictions
Choudhary, K. and DeCost, B. (2021) · 2021
Cited alongside, same era.
Jha, K., Saha, S., and Singh, H. (2022) · 2022
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Equivariant networks for crystal structures
Kaba, O. and Ravanbakhsh, S. (2022) · 2022
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Is machine learning redefining the perovskite solar cells?
Parikh, N., Karamta, M., Yadav, N., Mahdi Tavakoli, M., Prochowicz, D., Akin, S., Kalam, A., Satapathi, S., and Yadav, P. (2022) · 2022
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Advanced graph and sequence neural networks for molecular property prediction and drug discovery
Wang, Z., Liu, M., Luo, Y., Xu, Z., Xie, Y., Wang, L., Cai, L., Qi, Q., Yuan, Z., Yang, T., et al. (2022) · 2022
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Periodic graph transformers for crystal material property prediction
Yan, K., Liu, Y., Lin, Y., and Ji, S. (2022) · 2022
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Toward accurate interpretable predictions of materials properties within transformer language models
Korolev, V. and Protsenko, P. (2023) · 2023
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Leveraging language representation for material recommendation, ranking, and exploration
Qu, J., Xie, Y. R., and Ertekin, E. (2023) · 2023
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Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces
Réau, M., Renaud, N., Xue, L. C., and Bonvin, A. M. (2023) · 2023
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Song, Y., Miret, S., and Liu, B. (2023) · 2023
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