REFERENCES
1. Furukawa, H.; Cordova, K. E.; O’keeffe, M.; Yaghi, O. M. The chemistry and applications of metal-organic frameworks. Science 2013, 341, 1230444.
2. Feng, B.; Wang, B.; Lv, L.; et al. Interpreting X-ray diffraction patterns of metal-organic frameworks via generative artificial intelligence. J. Am. Chem. Soc. 2025, 148, 869-78.
3. Fu, X.; Xie, T.; Rosen, A. S.; Jaakkola, T.; Smith, J. MOFdiff: coarse-grained diffusion for metal-organic framework design. arXiv 2023, arXiv:2310.10732. Available online: https://arxiv.org/abs/2310.10732 (accessed 5 June 2026).
4. Fei, Y.; Mcdermott, M. J.; Rom, C. L.; Wang, S.; Ceder, G. Dara: automated multiple-hypothesis phase identification and refinement from powder X-ray diffraction. Chem. Mater. 2026, 38, 1364-76.
5. Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature 2023, 624, 86-91.
6. Oviedo, F.; Ren, Z.; Sun, S.; et al. Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks. npj. Comput. Mater. 2019, 5, 60.
7. Szymanski, N. J.; Bartel, C. J.; Zeng, Y.; Tu, Q.; Ceder, G. Probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra. Chem. Mater. 2021, 33, 4204-15.
8. Newbury, D. E.; Ritchie, N. W. M. Is scanning electron microscopy/energy dispersive X-ray spectrometry (SEM/EDS) quantitative? Scanning 2013, 35, 141-68.
