Authors: Juan Iván Gómez-Peralta*, Xim Bokhimi, Patricia Quintana-Owen
Email: gomezperalta.ai@gmail.com
Affiliations:
X.B.: Instituto de Física, Universidad Nacional Autónoma de México, A. P. 20-364, México 01000, México
J.I.G-P, P.Q.-O.: Laboratorio Nacional de Nano y Biomateriales, CINVESTAV-IPN, Antigua Carretera a Progreso km 6, A. P. 37, Cordemex Mérida, Yucatán 97310 México
References
[1] N.J. Szymanski, C.J. Bartel, Y. Zeng, Q. Tu, G. Ceder, Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra, Chemistry of Materials 33 (2021) 4204–4215. https://doi.org/10.1021/acs.chemmater.1c01071.
[2] Y. Xu, S. Wu, B. Lv, Y. Zhu, H. Zhang, S. Li, D. Dai, A Study on Phase Prediction Methods Based on Wavelet Transform and Deep Learning by XRD Data, JOM (2026). https://doi.org/10.1007/s11837-026-08265-x.
[3] J.W. Lee, W.B. Park, J.H. Lee, S.P. Singh, K.S. Sohn, A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns, Nat. Commun. 11 (2020). https://doi.org/10.1038/s41467-019-13749-3.
[4] L. Banko, P.M. Maffettone, D. Naujoks, D. Olds, A. Ludwig, Deep learning for visualization and novelty detection in large X-ray diffraction datasets, NPJ Comput. Mater. 7 (2021). https://doi.org/10.1038/s41524-021-00575-9.
[5] J. Greasley, P. Hosein, Exploring supervised machine learning for multi-phase identification and quantification from powder X-ray diffraction spectra, J. Mater. Sci. 58 (2023) 5334–5348. https://doi.org/10.1007/s10853-023-08343-4.
[6] G. Guo, J. Goldfeder, L. Lan, A. Ray, A.H. Yang, B. Chen, S.J.L. Billinge, H. Lipson, Towards end-to-end structure determination from x-ray diffraction data using deep learning, NPJ Comput. Mater. 10 (2024). https://doi.org/10.1038/s41524-024-01401-8.
[7] G. Guo, T.L. Saidi, M.W. Terban, M. Valsecchi, S.J.L. Billinge, H. Lipson, Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models, Nat. Mater. (2025). https://doi.org/10.1038/s41563-025-02220-y.
[8] E.A. Riesel, T. Mackey, H. Nilforoshan, M. Xu, C.K. Badding, A.B. Altman, J. Leskovec, D.E. Freedman, Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning, J. Am. Chem. Soc. (2024). https://doi.org/10.1021/jacs.4c10244.
[9] D.M. Melgalvis, T. Rekis, GraPhAI: Neural Networks for Solving Centrosymmetric Crystal Structures, J. Am. Chem. Soc. 148 (2026) 28754–28763. https://doi.org/10.1021/jacs.6c05607.
[10] W.B. Park, J. Chung, J. Jung, K. Sohn, S.P. Singh, M. Pyo, N. Shin, K.S. Sohn, Classification of crystal structure using a convolutional neural network, IUCrJ 4 (2017) 486–494. https://doi.org/10.1107/S205225251700714X.
[11] A. Chakraborty, R. Sharma, A deep crystal structure identification system for X-ray diffraction patterns, Vis. Comput. 38 (2022) 1275–1282. https://doi.org/10.1007/s00371-021-02165-8.
[12] P.M. Vecsei, K. Choo, J. Chang, T. Neupert, Neural network based classification of crystal symmetries from x-ray diffraction patterns, Phys. Rev. B 99 (2019) 245120. https://doi.org/10.1103/PhysRevB.99.245120.
[13] J.E. Salgado, S. Lerman, Z. Du, C. Xu, N. Abdolrahim, Automated classification of big X-ray diffraction data using deep learning models, NPJ Comput. Mater. 9 (2023). https://doi.org/10.1038/s41524-023-01164-8.
[14] Y. Suzuki, H. Hino, T. Hawai, K. Saito, M. Kotsugi, K. Ono, Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach, Sci. Rep. 10 (2020) 21790. https://doi.org/10.1038/s41598-020-77474-4.
[15] C.H. Liu, Y. Tao, D. Hsu, Q. Du, S.J.L. Billinge, Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function, Acta Crystallogr. A Found. Adv. 75 (2019) 633–643. https://doi.org/10.1107/S2053273319005606.
[16] S.R. Chitturi, D. Ratner, R.C. Walroth, V. Thampy, E.J. Reed, M. Dunne, C.J. Tassone, K.H. Stone, Automated prediction of lattice parameters from X-ray powder diffraction patterns, J. Appl. Crystallogr. 54 (2021) 1799–1810. https://doi.org/10.1107/S1600576721010840.
[17] S. Habershon, E.Y. Cheung, K.D.M. Harris, R.L. Johnston, Powder Diffraction Indexing as a Pattern Recognition Problem: A New Approach for Unit Cell Determination Based on an Artificial Neural Network, J. Phys. Chem. A 108 (2004) 711–716. https://doi.org/10.1021/jp0310596.
[18] J.I. Gómez-Peralta, X. Bokhimi, P. Quintana, Convolutional Neural Networks to Assist the Assessment of Lattice Parameters from X-ray Powder Diffraction, Journal of Physical Chemistry A 127 (2023) 7655–7664. https://doi.org/10.1021/acs.jpca.3c03860.
[19] J.I. Gómez-Peralta, X. Bokhimi, P. Quintana-Owen, Insights from the reciprocal space revealed by a convolutional neural network and transfer learning, Scr. Mater. 263 (2025). https://doi.org/10.1016/j.scriptamat.2025.116697.
[20] A. Souesme, R. Guinebretière, O. Castelnau, A. Boulle, Neural network determination of crystallite size and microstrain from X-ray powder diffraction data, n.d. https://pytorch.org/.
[21] J.I. Gómez-Peralta, X. Bokhimi, N.G. García-Peña, P. Quintana-Owen, G. Rodríguez-Gattorno, Band-gap assessment from X-ray powder diffraction using artificial intelligence, J. Appl. Crystallogr. 55 (2022) 1538–1548. https://doi.org/10.1107/S1600576722009797.
[22] J. Kim, L.C.O. Tiong, D. Kim, S.S. Han, Deep Learning-Based Prediction of Material Properties Using Chemical Compositions and Diffraction Patterns as Experimentally Accessible Inputs, J. Phys. Chem. Lett. 12 (2021) 8376–8383. https://doi.org/10.1021/acs.jpclett.1c02305.
[23] B. Do Lee, J.-W. Lee, W.B. Park, J. Park, M.-Y. Cho, S. Pal Singh, M. Pyo, K.-S. Sohn, Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction, Advanced Intelligent Systems 4 (2022) 2200042. https://doi.org/10.1002/aisy.202200042.
[24] A. Vaitkus, A. Merkys, S. Gražulis, Validation of the Crystallography Open Database using the Crystallographic Information Framework, J. Appl. Crystallogr. 54 (2021) 661–672. https://doi.org/10.1107/S1600576720016532.
[25] S. Gražulis, D. Chateigner, R.T. Downs, A.F.T. Yokochi, M. Quirós, L. Lutterotti, E. Manakova, J. Butkus, P. Moeck, A. Le Bail, Crystallography Open Database – an open-access collection of crystal structures, J. Appl. Crystallogr. 42 (2009) 726–729. https://doi.org/10.1107/S0021889809016690.
[26] S. Gražulis, A. Daškevič, A. Merkys, D. Chateigner, L. Lutterotti, M. Quirós, N.R. Serebryanaya, P. Moeck, R.T. Downs, A. Le Bail, Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration, Nucleic Acids Res. 40 (2012) D420–D427. https://doi.org/10.1093/nar/gkr900.
[27] A.A. Coelho, TOPAS and TOPAS-Academic : an optimization program integrating computer algebra and crystallographic objects written in C++, J. Appl. Crystallogr. 51 (2018) 210–218. https://doi.org/10.1107/S1600576718000183.
[28] A.S. Rosen, S.M. Iyer, D. Ray, Z. Yao, A. Aspuru-Guzik, L. Gagliardi, J.M. Notestein, R.Q. Snurr, Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery, Matter 4 (2021) 1578–1597. https://doi.org/10.1016/j.matt.2021.02.015.
[29] B. Olsthoorn, R.M. Geilhufe, S.S. Borysov, A. V. Balatsky, Band Gap Prediction for Large Organic Crystal Structures with Machine Learning, Adv. Quantum Technol. 2 (2019). https://doi.org/10.1002/qute.201900023.
[30] S. Kim, M. Lee, C. Hong, Y. Yoon, H. An, D. Lee, W. Jeong, D. Yoo, Y. Kang, Y. Youn, S. Han, A band-gap database for semiconducting inorganic materials calculated with hybrid functional, Sci. Data 7 (2020) 387. https://doi.org/10.1038/s41597-020-00723-8.
[31] J.I. Gómez Peralta, X. Bokhimi, P. Quintana-Owen, Variational autoencoders for generating multiphase powder diffraction patterns and their quantification, (2026). https://doi.org/10.2139/ssrn.6889867.
gomezperalta.mx 