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H. Rietveld, “Line profiles of neutron powder-diffraction peaks for structure refinement,” Acta Crystallographica , vol. 22, no. 1, pp. 151–152, 1967
1967
Earlier work this paper cites.
H. M. Rietveld, “A profile refinement method for nuclear and magnetic structures,” Journal of applied Crystallography , vol. 2, no. 2, pp. 65–71, 1969
1969
Earlier work this paper cites.
S. R. Hall, F. H. Allen, and I. D. Brown, “The crystallographic information file (cif): a new standard archive file for crystallography,” Foundations of Crystallography , vol. 47, no. 6, pp. 655–685, 1991
1991
Earlier work this paper cites.
H. Fjellvåg, “Symmetry-operations, point groups, space groups and crystal structure,” Department of Chemistry, University of Oslo , 1994
1994
Earlier work this paper cites.
M. Hellenbrandt, “The inorganic crystal structure database (icsd)—present and future,” Crystallography Reviews , vol. 10, no. 1, pp. 17–22, 2004
2004
Earlier work this paper cites.
F. H. Allen and R. Taylor, “Research applications of the cambridge structural database (csd),” Chemical Society Reviews , vol. 33, no. 8, pp. 463–475, 2004
2004
Earlier work this paper cites.
A. Hyvärinen, “Estimation of non-normalized statistical models by score matching,” Journal of Machine Learning Research , 2005
2005
Earlier work this paper cites.
C. W. Glass, A. R. Oganov, and N. Hansen, “Uspex—evolutionary crystal structure prediction,” Computer physics communications , vol. 175, no. 11-12, pp. 713–720, 2006
2006
Earlier work this paper cites.
V. K. Pecharsky and P. Y. Zavalij, Fundamentals of powder diffraction and structural characterization of materials . Springer, 2009
2009
Earlier work this paper cites.
P. Vincent, “A connection between score matching and denoising autoencoders,” Neural Computation , 2011
2011
Earlier work this paper cites.
S. Gražulis, A. Daškevič, A. Merkys, D. Chateigner, L. Lutterotti, M. Quiros, N. R. Serebryanaya, P. Moeck, R. T. Downs, and A. Le Bail, “Crystallography open database (cod): an open-access collection of crystal structures and platform for world-wide collaboration,” Nucleic acids research , vol. 40, no. D1, pp. D420–D427, 2012
2012
Earlier work this paper cites.
S. Curtarolo, W. Setyawan, G. L. Hart, M. Jahnatek, R. V. Chepulskii, R. H. Taylor, S. Wang, J. Xue, K. Yang, O. Levy et al. , “Aflow: An automatic framework for high-throughput materials discovery,” Computational Materials Science , vol. 58, pp. 218–226, 2012
2012
Earlier work this paper cites.
Y. Wang, J. Lv, L. Zhu, and Y. Ma, “Calypso: A method for crystal structure prediction,” Computer Physics Communications , vol. 183, no. 10, pp. 2063–2070, 2012
2012
Earlier work this paper cites.
S. Curtarolo, G. L. Hart, M. B. Nardelli, N. Mingo, S. Sanvito, and O. Levy, “The high-throughput highway to computational materials design,” Nature materials , vol. 12, no. 3, pp. 191–201, 2013
2013
Earlier work this paper cites.
D. P. Kingma, M. Welling et al. , “Auto-encoding variational bayes,” 2013
2013
Earlier work this paper cites.
B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, “Combinatorial screening for new materials in unconstrained composition space with machine learning,” Physical Review B , vol. 89, no. 9, p. 094104, 2014
2014
Earlier work this paper cites.
J. An and S. Cho, “Variational autoencoder based anomaly detection using reconstruction probability,” Special lecture on IE , vol. 2, no. 1, pp. 1–18, 2015
2015
Earlier work this paper cites.
D. P. Kingma, T. Salimans, and M. Welling, “Variational dropout and the local reparameterization trick,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in ICML , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. O. Oliynyk, E. Antono, T. D. Sparks, L. Ghadbeigi, M. W. Gaultois, B. Meredig, and A. Mar, “High-throughput machine-learning-driven synthesis of full-heusler compounds,” Chemistry of Materials , vol. 28, no. 20, pp. 7324–7331, 2016
2016
Earlier work this paper cites.
P. Raccuglia, K. C. Elbert, P. D. Adler, C. Falk, M. B. Wenny, A. Mollo, M. Zeller, S. A. Friedler, J. Schrier, and A. J. Norquist, “Machine-learning-assisted materials discovery using failed experiments,” Nature , vol. 533, no. 7601, pp. 73–76, 2016
2016
Earlier work this paper cites.
L. Ward, A. Agrawal, A. Choudhary, and C. Wolverton, “A general-purpose machine learning framework for predicting properties of inorganic materials,” npj Computational Materials , vol. 2, no. 1, pp. 1–7, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim, “Machine learning in materials informatics: recent applications and prospects,” npj Computational Materials , vol. 3, no. 1, p. 54, 2017
2017
Earlier work this paper cites.
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-vae: Learning basic visual concepts with a constrained variational framework,” in International conference on learning representations , 2017
2017
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1125–1134
2017
Earlier work this paper cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International conference on machine learning . PMLR, 2017, pp. 214–223
2017
Earlier work this paper cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in ICML , vol. 70. PMLR, 2017, pp. 1263–1272
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
H. Huang, R. He, Z. Sun, T. Tan et al. , “Introvae: Introspective variational autoencoders for photographic image synthesis,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, “Stackgan++: Realistic image synthesis with stacked generative adversarial networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 8, pp. 1947–1962, 2018
2018
Earlier work this paper cites.
T. Xie and J. C. Grossman, “Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,” Physical review letters , vol. 120, no. 14, p. 145301, 2018
2018
Earlier work this paper cites.
C. Draxl and M. Scheffler, “The nomad laboratory: from data sharing to artificial intelligence,” Journal of Physics: Materials , vol. 2, no. 3, p. 036001, 2019
2019
Earlier work this paper cites.
C. Chen, W. Ye, Y. Zuo, C. Zheng, and S. P. Ong, “Graph networks as a universal machine learning framework for molecules and crystals,” Chemistry of Materials , vol. 31, no. 9, pp. 3564–3572, 2019
2019
Earlier work this paper cites.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” in NeurIPS , 2019
2019
Earlier work this paper cites.
K. Kumar, R. Kumar, T. De Boissiere, L. Gestin, W. Z. Teoh, J. Sotelo, A. De Brebisson, Y. Bengio, and A. C. Courville, “Melgan: Generative adversarial networks for conditional waveform synthesis,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
J. Noh, J. Kim, H. S. Stein, B. Sanchez-Lengeling, J. M. Gregoire, A. Aspuru-Guzik, and Y. Jung, “Inverse Design of Solid-State Materials via a Continuous Representation,” Matter , 2019
2019
Earlier work this paper cites.
N. Keriven and G. Peyré, “Universal invariant and equivariant graph neural networks,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
N. Gebauer, M. Gastegger, and K. Schütt, “Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
D. W. Davies, K. T. Butler, A. J. Jackson, J. M. Skelton, K. Morita, and A. Walsh, “Smact: Semiconducting materials by analogy and chemical theory,” Journal of Open Source Software , vol. 4, no. 38, p. 1361, 2019
2019
Earlier work this paper cites.
A. Jain, J. Montoya, S. Dwaraknath, N. E. Zimmermann, J. Dagdelen, M. Horton, P. Huck, D. Winston, S. Cholia, S. P. Ong et al. , “The materials project: Accelerating materials design through theory-driven data and tools,” Handbook of Materials Modeling: Methods: Theory and Modeling , pp. 1751–1784, 2020
2020
Earlier work this paper cites.
Z. Allahyari and A. R. Oganov, “Coevolutionary search for optimal materials in the space of all possible compounds,” npj Computational Materials , vol. 6, no. 1, p. 55, 2020
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in NeurIPS , 2020
2020
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
S. Kim, J. Noh, G. H. Gu, A. Aspuru-Guzik, and Y. Jung, “Generative Adversarial Networks for Crystal Structure Prediction,” ACS Central Science , 2020
2020
Earlier work this paper cites.
V. Garg, S. Jegelka, and T. Jaakkola, “Generalization and representational limits of graph neural networks,” in International conference on machine learning . PMLR, 2020, pp. 3419–3430
2020
Cited alongside, same era.
C. J. Court, B. Yildirim, A. Jain, and J. M. Cole, “3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning,” Journal of Chemical Information and Modeling , 2020
2020
Cited alongside, same era.
B. Kim, S. Lee, and J. Kim, “Inverse design of porous materials using artificial neural networks,” Science Advances , 2020
2020
Cited alongside, same era.
Y. Dan, Y. Zhao, X. Li, S. Li, M. Hu, and J. Hu, “Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse design of inorganic materials,” npj Computational Materials , vol. 6, no. 1, p. 84, 2020
2020
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
S. Zhang, B. Cao, T. Su, Y. Wu, Z. Feng, J. Xiong, and T.-Y. Zhang, “Crystallographic phase identifier of a convolutional self-attention neural network (cpicann) on powder diffraction patterns,” IUCrJ , vol. 11, no. Pt 4, p. 634, 2024
2024
Later among the works it cites.
B. K. Miller, R. T. Chen, A. Sriram, and B. M. Wood, “Flowmm: Generating materials with riemannian flow matching,” in Forty-first International Conference on Machine Learning , 2024
2024
Later among the works it cites.
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K. Choudhary, K. F. Garrity, A. C. Reid, B. DeCost, A. J. Biacchi, A. R. Hight Walker, Z. Trautt, J. Hattrick-Simpers, A. G. Kusne, A. Centrone et al. , “The joint automated repository for various integrated simulations (jarvis) for data-driven materials design,” npj computational materials , vol. 6, no. 1, p. 173, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Luo, K. Yan, and S. Ji, “Graphdf: A discrete flow model for molecular graph generation,” in International conference on machine learning . PMLR, 2021, pp. 7192–7203
2021
Cited alongside, same era.
L. Chanussot, A. Das, S. Goyal, T. Lavril, M. Shuaibi, M. Riviere, K. Tran, J. Heras-Domingo, C. Ho, W. Hu et al. , “Open catalyst 2020 (oc20) dataset and community challenges,” Acs Catalysis , vol. 11, no. 10, pp. 6059–6072, 2021
2021
Cited alongside, same era.
O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Schutt, A. Tkatchenko, and K.-R. M”uller, “Machine learning force fields,” Chemical Reviews , vol. 121, no. 16, pp. 10 142–10 186, 2021
2021
Cited alongside, same era.
K. Guo, Z. Yang, C.-H. Yu, and M. J. Buehler, “Artificial intelligence and machine learning in design of mechanical materials,” Materials Horizons , vol. 8, no. 4, pp. 1153–1172, 2021
2021
Cited alongside, same era.
J. Huang, J. Liew, A. Ademiloye, and K. M. Liew, “Artificial intelligence in materials modeling and design,” Archives of Computational Methods in Engineering , vol. 28, pp. 3399–3413, 2021
2021
Cited alongside, same era.
J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” in International Conference on Machine Learning . PMLR, 2021, pp. 5530–5540
2021
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Zheng, Y. Liu, J. Li, J. Yao, and Y. Rong, “Relaxing continuous constraints of equivariant graph neural networks for broad physical dynamics learning,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 4548–4558
2024
Later among the works it cites.
2024
Later among the works it cites.
K. Yan, X. Li, H. Ling, K. Ashen, C. Edwards, R. Arróyave, M. Zitnik, H. Ji, X. Qian, X. Qian et al. , “Invariant tokenization of crystalline materials for language model enabled generation,” Advances in Neural Information Processing Systems , vol. 37, pp. 125 050–125 072, 2024
2024
Later among the works it cites.
S. Yang, S. Batzner, R. Gao, M. Aykol, A. Gaunt, B. C. McMorrow, D. Jimenez Rezende, D. Schuurmans, I. Mordatch, and E. D. Cubuk, “Generative hierarchical materials search,” Advances in Neural Information Processing Systems , vol. 37, pp. 38 799–38 819, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
A. Sriram, B. Miller, R. T. Chen, and B. Wood, “Flowllm: Flow matching for material generation with large language models as base distributions,” Advances in Neural Information Processing Systems , vol. 37, pp. 46 025–46 046, 2024
2024
Later among the works it cites.
R. Zhu, W. Nong, S. Yamazaki, and K. Hippalgaonkar, “WyCryst: Wyckoff inorganic crystal generator framework,” Matter , 2024
2024
Later among the works it cites.
S. Mal, G. Seal, and P. Sen, “MagGen: A Graph-Aided Deep Generative Model for Inverse Design of Permanent Magnets,” The Journal of Physical Chemistry Letters , 2024
2024
Later among the works it cites.
Z. Chen, H. Li, C. Zhang, H. Zhang, Y. Zhao, J. Cao, T. He, L. Xu, H. Xiao, Y. Li, H. Shao, X. Yang, X. He, and G. Fang, “Crystal Structure Prediction Using Generative Adversarial Network with Data-Driven Latent Space Fusion Strategy,” Journal of Chemical Theory and Computation , 2024
2024
Later among the works it cites.
Z. Li and N. Birbilis, “NSGAN: a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery,” npj Computational Materials , 2024
2024
Later among the works it cites.
Z. Ye, N. Wang, J. Zhou, and D. Ouyang, “Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks,” The Innovation , vol. 5, no. 2, 2024
2024
Later among the works it cites.
T. Su, B. Cao, S. Hu, M. Li, and T.-Y. Zhang, “Cgwgan: crystal generative framework based on wyckoff generative adversarial network,” Journal of Materials Informatics , vol. 4, no. 4, pp. N–A, 2024
2024
Later among the works it cites.
X. Luo, Z. Wang, P. Gao, J. Lv, Y. Wang, C. Chen, and Y. Ma, “Deep learning generative model for crystal structure prediction,” npj Computational Materials , 2024
2024
Later among the works it cites.
C.-Y. Ye, H.-M. Weng, and Q.-S. Wu, “Con-cdvae: A method for the conditional generation of crystal structures,” Computational Materials Today , 2024
2024
Later among the works it cites.
A. Sinha, S. Jia, and V. Fung, “Representation-space diffusion models for generating periodic materials,” 2024
2024
Later among the works it cites.
A. Klipfel, Y. Fregier, A. Sayede, and Z. Bouraoui, “Vector field oriented diffusion model for crystal material generation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 20, 2024, pp. 22 193–22 201
2024
Later among the works it cites.
P. Lin, P. Chen, R. Jiao, Q. Mo, C. Jianhuan, W. Huang, Y. Liu, D. Huang, and Y. Lu, “Equivariant diffusion for crystal structure prediction,” in Forty-first International Conference on Machine Learning , 2024
2024
Later among the works it cites.
S. Yuan and S. Dordevic, “Diffusion models for conditional generation of hypothetical new families of superconductors,” Scientific Reports , vol. 14, no. 1, p. 10275, 2024
2024
Later among the works it cites.
L. M. Antunes, K. T. Butler, and R. Grau-Crespo, “Crystal structure generation with autoregressive large language modeling,” Nature Communications , vol. 15, no. 1, pp. 1–16, 2024
2024
Later among the works it cites.
E. T. Chenebuah, M. Nganbe, and A. B. Tchagang, “A deep generative modeling architecture for designing lattice-constrained perovskite materials,” npj Computational Materials , 2024
2024
Later among the works it cites.
C. Qin, J. Liu, S. Ma, J. Du, G. Jiang, and L. Zhao, “Inverse design of semiconductor materials with deep generative models,” Journal of Materials Chemistry A , 2024
2024
Later among the works it cites.
T. Pakornchote, N. Choomphon-anomakhun, S. Arrerut, C. Atthapak, S. Khamkaeo, T. Chotibut, and T. Bovornratanaraks, “Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling,” Scientific Reports , 2024
2024
Later among the works it cites.
T. Li, B. Cao, T. Su, L. Lin, D. Wang, X. Liu, H. Wan, H. Ji, Z. He, Y. Chen et al. , “Machine learning-engineered nanozyme system for synergistic anti-tumor ferroptosis/apoptosis therapy,” Small , vol. 21, no. 5, p. 2408750, 2025
2025
Closest in time.
B. Cao and T.-Y. Zhang, “Hkust-crystdb (revision eaf5862),” https://huggingface.co/datasets/caobin/HKUST-CrystDB , 2025
2025
Closest in time.
B. Cao, D. Anderson, and L. Davis, “Asugnn: an asymmetric-unit-based graph neural network for crystal property prediction,” Applied Crystallography , vol. 58, no. 1, 2025
2025
Closest in time.
Z. Chen, Z. Meng, T. He, H. Li, J. Cao, L. Xu, H. Xiao, Y. Zhang, X. He, and G. Fang, “Crystal structure prediction meets artificial intelligence,” The Journal of Physical Chemistry Letters , vol. 16, no. 10, pp. 2581–2591, 2025
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
C. Zeni, R. Pinsler, D. Zügner, A. Fowler, M. Horton, X. Fu, Z. Wang, A. Shysheya, J. Crabbé, S. Ueda et al. , “A generative model for inorganic materials design,” Nature , pp. 1–3, 2025
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
K. Das, S. Khastagir, P. Goyal, S.-C. Lee, S. Bhattacharjee, and N. Ganguly, “Periodic materials generation using text-guided joint diffusion model,” in International Conference on Learning Representations , 2025
2025
Closest in time.
2025
Closest in time.
H. Wu, Y. Song, J. Gong, Z. Cao, Y. Ouyang, J. Zhang, H. Zhou, W.-Y. Ma, and J. Liu, “A periodic bayesian flow for material generation,” in International Conference on Learning Representations , 2025
2025
Closest in time.
International Centre for Diffraction Data, “Icdd - international centre for diffraction data,” 2025, accessed: 2025-04-18. [Online]. Available: https://www.icdd.com/
2025
Closest in time.