Page 98 - Read Online
P. 98

Page 20 of 20                      Gesch et al. J. Mater. Inf. 2025, 5, 42  https://dx.doi.org/10.20517/jmi.2025.23

                   physical vapor deposition. Acta. Mater. 2020, 188, 181-91.  DOI
               56.      Monti, J. M.; Hopkins, E. M.; Hattar, K.; Abdeljawad, F.; Boyce, B. L.; Dingreville, R. Stability of immiscible nanocrystalline alloys
                   in compositional and thermal fields. Acta. Mater. 2022, 226, 117620.  DOI
               57.      Blaiszik, B.; Chard, K.; Pruyne, J.; Ananthakrishnan, R.; Tuecke, S.; Foster, I. The materials data facility: data services to advance
                   materials science research. JOM. 2016, 68, 2045-52.  DOI
               58.      Blaiszik, B.; Ward, L.; Schwarting, M.; et al. A data ecosystem to support machine learning in materials science. MRS. Commun. 2019,
                   9, 1125-33.  DOI
               59.      Gesch, A. H.; Hu, C. Ostwald Ripening Dataset for “Accelerating phase-field simulation of coupled microstructural evolution using
                   autoencoder-based recurrent neural networks”. 2025.  DOI
               60.      Cho, K.; van, M. B.; Gulcehre, C.; et al. Learning phrase representations using RNN encoder-decoder for statistical machine
                   translation. arXiv 2014, arXiv:1406.1078. https://doi.org/10.48550/arXiv.1406.1078. (accessed 9 Jun 2025)
               61.      Desai, S.; Shrivastava, A.; D’elia, M.; Najm, H. N.; Dingreville, R. Trade-offs in the latent representation of microstructure evolution.
                   Acta. Mater. 2024, 263, 119514.  DOI
               62.      Bank, D.; Koenigstein, N.; Giryes, R. Autoencoders. In: Rokach L, Maimon O, Shmueli E, editors. Machine Learning For Data
                   Science Handbook. Cham: Springer International Publishing; 2023. pp. 353-74.  DOI
               63.      Hu, A.; Liu, Z.; Chen, Q.; et al. A new framework for predicting tensile stress of natural rubber based on data augmentation and
                   molecular dynamics simulation data. J. Mater. Inf. 2024, 4, 11.  DOI
               64.      Chowdhury, A.; Kautz, E.; Yener, B.; Lewis, D. Image driven machine learning methods for microstructure recognition. Comput.
                   Mater. Sci. 2016, 123, 176-87.  DOI
               65.      Chan, H.; Cherukara, M.; Loeffler, T. D.; Narayanan, B.; Sankaranarayanan, S. K. R. S. Machine learning enabled autonomous
                   microstructural characterization in 3D samples. npj. Comput. Mater. 2020, 6, 267.  DOI
               66.      Creswell, A.; Arulkumaran, K.; Bharath, A. A. On denoising autoencoders trained to minimise binary cross-entropy. arXiv 2017,
                   arXiv:1708.08487. https://doi.org/10.48550/arXiv.1708.08487. (accessed 9 Jun 2025)
               67.      Tealab, A. Time series forecasting using artificial neural networks methodologies: a systematic review. Future. Comput. Informatics.
                   J. 2018, 3, 334-40.  DOI
               68.      Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural. Comput. 1997, 9, 1735-80.  DOI  PubMed
               69.      Naser, M. Z.; Alavi, A. H. Error metrics and performance fitness indicators for artificial intelligence and machine learning in
                   engineering and sciences. Archit. Struct. Constr. 2023, 3, 499-517.  DOI
               70.      Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-97.  DOI
               71.      Zhu, J. Z.; Wang, T.; Ardell, A. J.; Zhou, S. H.; Liu, Z. K.; Chen, L. Q. Three-dimensional phase-field simulations of coarsening
                   kinetics of γ′ particles in binary Ni–Al alloys. Acta. Mater. 2004, 52, 2837-45.  DOI
   93   94   95   96   97   98   99   100   101   102   103