A Machine Learning Framework for Predicting the Glass Transition Temperature of Homopolymers
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Aharoni S., 1998, Polym. Adv. Technol., 9, 169, 10.1002/(SICI)1099-1581(199803)9:3<169::AID-PAT740>3.0.CO;2-Z
John N., 2017, The Chemistry of Polymers, 1, 5
Winston P. H., 1992, Artificial Intelligence, 3
Jozef B., 1992, Computational Modeling of Polymers (Plastics Engineering), 1
Jozef B., 2002, Prediction of Polymer Properties, 3
Goodfellow I., 2016, Deep Learning, 1
2022, Graph Neural Networks: Foundations, Frontiers, and Applications, 1, 1
Vaswani A., 2017, Adv. Neural Inf. Process. Syst.
Bhardwaj A., 2018, Deep Learning Essentials: Your Hands-on Guide to the Fundamentals of Deep Learning and Neural Network Modeling, 1, 1
Duvenaud D., 2015, Adv. Neural Inf. Process. Syst.
Gilmer J., 2017, International conference on machine learning
Glass Transition Temperatures. CROW. http://polymerdatabase.com (accessed 2022-02-02).
Landrum G., 2006, Open-Source Cheminformatics and Machine Learning
Provost, F. Machine Learning from Imbalanced Data Sets 101. In Proceedings of the AAAI’2000 workshop on imbalanced data sets; AAAI Press: 2000; pp. 1-3.
Goldberg D., 1988, Genetic Algorithms in Search, Optimization and Machine Learning, 13
Auer P., 2002, J. Mach. Learn. Res., 3, 397
Papp P. A., 2021, Adv. Neural Inf. Process. Syst., 34, 21997
Bennett K. P., 2006, J. Mach. Learn. Res., 7, 1265
Chollet F., 2021, Deep Learning with Python, Second Edition