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Accurate and efficient prediction of polymer properties is of great significance in polymer design.
Brown, T., et al. Language models are few-shot learners. Advances in neural information processing systems 2020
1901
Earlier work this paper cites.
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. 1988
1988
Earlier work this paper cites.
Landrum, G., et al. RDKit: Open-source cheminformatics. https://www.rdkit.org 2006
2006
Earlier work this paper cites.
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; Monfardini, G. The graph neural network model. IEEE trans. neural netw. 2008
2008
Earlier work this paper cites.
Van der Maaten, L.; Hinton, G. Visualizing data using t-SNE. J Mach Learn Res 2008
2008
Earlier work this paper cites.
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J Chem Inf Model 2010
2010
Earlier work this paper cites.
Eyben, F.; Wöllmer, M.; Schuller, B. Opensmile: the munich versatile and fast open-source audio feature extractor. Proceedings of the 18th ACM international conference on Multimedia. 2010; pp 1459–1462
2010
Earlier work this paper cites.
Otsuka, S.; Kuwajima, I.; Hosoya, J.; Xu, Y.; Yamazaki, M. PoLyInfo: Polymer database for polymeric materials design. 2011 International Conference on Emerging Intelligent Data and Web Technologies. 2011; pp 22–29
2011
Earlier work this paper cites.
Le, T.; Epa, V. C.; Burden, F. R.; Winkler, D. A. Quantitative structure–property relationship modeling of diverse materials properties. Chem. Rev. 2012
2012
Earlier work this paper cites.
Cho, K., et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. ACL Anthology 2014
2014
Earlier work this paper cites.
Cadeddu, A.; Wylie, E. K.; Jurczak, J.; Wampler-Doty, M.; Grzybowski, B. A. Organic chemistry as a language and the implications of chemical linguistics for structural and retrosynthetic analyses. Angew. Chem. Int. Ed. 2014
2014
Earlier work this paper cites.
Duvenaud, D. K., et al. Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems 2015
2015
Earlier work this paper cites.
Cereto-Massagué, A., et al. Molecular fingerprint similarity search in virtual screening. Methods 2015
2015
Earlier work this paper cites.
Persson, N.; McBride, M.; Grover, M.; Reichmanis, E. Silicon valley meets the ivory tower: Searchable data repositories for experimental nanomaterials research. Curr Opin Solid State Mater Sci 2016
2016
Earlier work this paper cites.
Vaswani, A., et al. Attention is all you need. Advances in neural information processing systems 2017
2017
Earlier work this paper cites.
Luo, H., et al. Core–shell nanostructure design in polymer nanocomposite capacitors for energy storage applications. ACS Sustain. Chem. Eng. 2018
2018
Earlier work this paper cites.
Mannodi-Kanakkithodi, A., et al. Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond. Mater. Today 2018
2018
Earlier work this paper cites.
Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 2018
2018
Earlier work this paper cites.
Schwaller, P.; Gaudin, T.; Lanyi, D.; Bekas, C.; Laino, T. “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. Chem. Sci. 2018
2018
Earlier work this paper cites.
Peters, M. E.; Neumann, M.; Zettlemoyer, L.; Yih, W.-t. Dissecting Contextual Word Embeddings: Architecture and Representation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018; pp 1499–1509
2018
Earlier work this paper cites.
Nagasawa, S.; Al-Naamani, E.; Saeki, A. Computer-aided screening of conjugated polymers for organic solar cell: classification by random forest. J. Phys. Chem. Lett. 2018
2018
Earlier work this paper cites.
Kim, C.; Chandrasekaran, A.; Huan, T. D.; Das, D.; Ramprasad, R. Polymer genome: a data-powered polymer informatics platform for property predictions. J. Phys. Chem. C 2018
2018
Earlier work this paper cites.
St. John, P. C., et al. Message-passing neural networks for high-throughput polymer screening. J. Chem. Phys. 2019
2019
Earlier work this paper cites.
Bai, Y., et al. Accelerated discovery of organic polymer photocatalysts for hydrogen evolution from water through the integration of experiment and theory. J. Am. Chem. Soc. 2019
2019
Earlier work this paper cites.
Yang, K., et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 2019
2019
Earlier work this paper cites.
Lin, T.-S., et al. BigSMILES: a structurally-based line notation for describing macromolecules. ACS Cent. Sci. 2019
2019
Cited alongside, same era.
Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT. 2019; pp 4171–4186
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Conneau, A.; Lample, G. Cross-lingual language model pretraining. Advances in neural information processing systems 2019
2019
Cited alongside, same era.
Munshi, J.; Chen, W.; Chien, T.; Balasubramanian, G. Transfer learned designer polymers for organic solar cells. J. Chem. Inf. Model. 2021
2021
Later among the works it cites.
Liang, J.; Xu, S.; Hu, L.; Zhao, Y.; Zhu, X. Machine-learning-assisted low dielectric constant polymer discovery. Mater. Chem. Front. 2021
2021
Later among the works it cites.
Chen, L., et al. Polymer informatics: Current status and critical next steps. Mater. Sci. Eng. R Rep. 2021
2021
Later among the works it cites.
Rahman, A., et al. A machine learning framework for predicting the shear strength of carbon nanotube-polymer interfaces based on molecular dynamics simulation data. Compos Sci Technol 2021
2021
Later among the works it cites.
Goswami, S.; Ghosh, R.; Neog, A.; Das, B. Deep learning based approach for prediction of glass transition temperature in polymers. Mater. Today: Proc. 2021
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2019
Cited alongside, same era.
Wang, S.; Guo, Y.; Wang, Y.; Sun, H.; Huang, J. SMILES-BERT: large scale unsupervised pre-training for molecular property prediction. Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics. 2019; pp 429–436
2019
Cited alongside, same era.
Schwaller, P., et al. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci. 2019
2019
Cited alongside, same era.
Poličar, P. G.; Stražar, M.; Zupan, B. openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding. Preprint at https://www.biorxiv.org/content/10.1101/731877v3.abstract 2019
2019
Cited alongside, same era.
Wang, Y., et al. Toward designing highly conductive polymer electrolytes by machine learning assisted coarse-grained molecular dynamics. Chem. Mater. 2020
2020
Cited alongside, same era.
Hu, H., et al. Recent advances in rational design of polymer nanocomposite dielectrics for energy storage. Nano Energy 2020
2020
Cited alongside, same era.
Karamad, M., et al. Orbital graph convolutional neural network for material property prediction. Phys. Rev. Mater. 2020
2020
Cited alongside, same era.
Tsai, S.-T.; Kuo, E.-J.; Tiwary, P. Learning molecular dynamics with simple language model built upon long short-term memory neural network. Nat. Commun. 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
Ying, C., et al. Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems 2021
2021
Later among the works it cites.
Schauser, N. S.; Kliegle, G. A.; Cooke, P.; Segalman, R. A.; Seshadri, R. Database creation, visualization, and statistical learning for polymer Li+-electrolyte design. Chem. Mater. 2021
2021
Later among the works it cites.
Kuenneth, C., et al. Polymer informatics with multi-task learning. Patterns 2021
2021
Later among the works it cites.
Yang, Z.; Yang, Y.; Cer, D.; Law, J.; Darve, E. Universal Sentence Representation Learning with Conditional Masked Language Model. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021; pp 6216–6228
2021
Later among the works it cites.
Hao, Y.; Dong, L.; Wei, F.; Xu, K. Self-attention attribution: Interpreting information interactions inside transformer. Proceedings of the AAAI Conference on Artificial Intelligence. 2021; pp 12963–12971
2021
Later among the works it cites.
Reis, M., et al. Machine-learning-guided discovery of 19F MRI agents enabled by automated copolymer synthesis. J. Am. Chem. Soc. 2021
2021
Later among the works it cites.
Chen, G.; Tao, L.; Li, Y. Predicting polymers’ glass transition temperature by a chemical language processing model. Polymers 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Xie, T., et al. Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties. Nat. Commun. 2022
2022
Closest in time.
Wang, Y.; Wang, J.; Cao, Z.; Barati Farimani, A. Molecular contrastive learning of representations via graph neural networks. Nat. Mach. Intell. 2022
2022
Closest in time.
Park, J., et al. Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network. ACS polym. Au. 2022
2022
Closest in time.
Aldeghi, M.; Coley, C. W. A graph representation of molecular ensembles for polymer property prediction. Chem. Sci. 2022
2022
Closest in time.
Flam-Shepherd, D.; Zhu, K.; Aspuru-Guzik, A. Language models can learn complex molecular distributions. Nat. Commun. 2022
2022
Closest in time.
Patel, R. A.; Borca, C. H.; Webb, M. A. Featurization strategies for polymer sequence or composition design by machine learning. Mol. Syst. Des. Eng. 2022
2022
Closest in time.
Bhattacharya, D.; Kleeblatt, D. C.; Statt, A.; Reinhart, W. F. Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks. Soft Matter 2022
2022
Closest in time.
Irwin, R.; Dimitriadis, S.; He, J.; Bjerrum, E. J. Chemformer: a pre-trained transformer for computational chemistry. Mach. learn.: sci. technol. 2022
2022
Closest in time.
Magar, R.; Wang, Y.; Barati Farimani, A. Crystal twins: self-supervised learning for crystalline material property prediction. NPJ Comput. Mater. 2022
2022
Closest in time.
Tamasi, M. J., et al. Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids. Adv. Mater. 2022
2022
Closest in time.
Cao, Z.; Magar, R.; Wang, Y.; Barati Farimani, A. MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction. J. Am. Chem. Soc. 2023
2023
Closest in time.