Workshop on Glass Modelling via Neural Networks and Data-Driven Glass Science

Author: Daniel R. Cassar.

Workshop dates: 09/09 to 12/09.

Location: Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.

Slides and Jupyter notebooks

The slides and Jupyter notebooks are in the resources folder.

References for GlassNet and ViscNet

1. D.R. Cassar, GlassNet: A multitask deep neural network for predicting many glass properties, Ceramics International 49 (2023) 36013–36024. https://doi.org/10.1016/j.ceramint.2023.08.281.
2. D.R. Cassar, ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity, Acta Materialia 206 (2021) 116602. https://doi.org/10.1016/j.actamat.2020.116602.

Study material

1. Karpathy, A. Neural Networks: Zero to Hero. https://karpathy.ai/zero-to-hero.html
2. Deisenroth, M.P., Faisal, A.A., and Ong, C.S. (2021). Mathematics for Machine Learning. https://mml-book.com
3. James, G., Witten, D., Hastie, T., Tibshirani, R., and Taylor, J. (2023). An Introduction to Statistical Learning: with Applications in Python. https://www.statlearning.com/
4. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition 2nd ed. 2009, Corr. 9th printing 2017 Edição. (Springer). https://hastie.su.domains/ElemStatLearn/
5. Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep learning (MIT press Cambridge). https://www.deeplearningbook.org/
6. Bishop, C.M. (2023). Deep Learning: Foundations and Concepts https://issuu.com/cmb321/docs/deep_learning_ebook
7. S. Raschka, Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning, (2020). https://doi.org/10.48550/arXiv.1811.12808.

Acknowledgements

• Bundesanstalt für Materialforschung und -prüfung (BAM)
• INCT Materials Informatics
• National Council for Scientific and Technological Development (CNPq)