Fetching the paper…
Reading the bibliography…
Artificial intelligence (AI) is transforming materials science, enabling both theoretical advancements and accelerated materials discovery.
R. Hill, The Elastic Behaviour of a Crystalline Aggregate, Proceedings of the Physical Society. Section A 65
1952
Earlier work this paper cites.
H. J. Monkhorst and J. D. Pack, Special points for Brillouin-zone integrations, Physical Review B 13
1976
Earlier work this paper cites.
P. E. Blöchl, Projector augmented-wave method, Physical Review B 50
1994
Earlier work this paper cites.
J. P. Perdew, K. Burke, and M. Ernzerhof, Generalized Gradient Approximation Made Simple, Physical Review Letters 77
1996
Earlier work this paper cites.
Y. Wang, J. Lv, L. Zhu, and Y. Ma, Crystal structure prediction via particle-swarm optimization, Physical Review B—Condensed Matter and Materials Physics 82
2010
Earlier work this paper cites.
A. R. Oganov, A. O. Lyakhov, and M. Valle, How evolutionary crystal structure prediction works–and why, Accounts of chemical research 44
2011
Earlier work this paper cites.
S. P. Ong, W. D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V. L. Chevrier, K. A. Persson, and G. Ceder, Python materials genomics (pymatgen): A robust, open-source python library for materials analysis, Computational Materials Science 68
2013
Earlier work this paper cites.
F. Mouhat and F. m. c.-X. Coudert, Necessary and sufficient elastic stability conditions in various crystal systems, Phys. Rev. B 90
2014
Earlier work this paper cites.
M. De Jong, W. Chen, T. Angsten, A. Jain, R. Notestine, A. Gamst, M. Sluiter, C. Krishna Ande, S. Van Der Zwaag, J. J. Plata, et al. , Charting the complete elastic properties of inorganic crystalline compounds, Scientific data 2
2015
Earlier work this paper cites.
A. P. Bartók, S. De, C. Poelking, N. Bernstein, J. R. Kermode, G. Csányi, and M. Ceriotti, Machine learning unifies the modeling of materials and molecules, Science advances 3
2017
Earlier work this paper cites.
K. Schütt, P.-J. Kindermans, H. E. Sauceda Felix, S. Chmiela, A. Tkatchenko, and K.-R. Müller, Schnet: A continuous-filter convolutional neural network for modeling quantum interactions, Advances in neural information processing systems 30
2017
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 120
2018
Earlier work this paper cites.
L. Himanen, A. Geurts, A. S. Foster, and P. Rinke, Data-driven materials science: status, challenges, and perspectives, Advanced Science 6
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 31
2019
Cited alongside, same era.
A. Dunn, Q. Wang, A. Ganose, D. Dopp, and A. Jain, Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm, npj Computational Materials 6
2020
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 11
2021
Cited alongside, same era.
2021
Cited alongside, same era.
R. Jiao, W. Huang, P. Lin, J. Han, P. Chen, Y. Lu, and Y. Liu, Crystal structure prediction by joint equivariant diffusion, Advances in Neural Information Processing Systems 36
2023
Later among the works it cites.
2023
Later among the works it cites.
R. Jin, X. Yuan, and E. Gao, Atomic stiffness for bulk modulus prediction and high-throughput screening of ultraincompressible crystals, Nature Communications 14
2023
Later among the works it cites.
D. Zhang, X. Liu, X. Zhang, C. Zhang, C. Cai, H. Bi, Y. Du, X. Qin, A. Peng, J. Huang, et al. , Dpa-2: a large atomic model as a multi-task learner, npj Computational Materials 10
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Zhao, M. Al-Fahdi, M. Hu, E. M. Siriwardane, Y. Song, A. Nasiri, and J. Hu, High-throughput discovery of novel cubic crystal materials using deep generative neural networks, Advanced Science 8
2021
Cited alongside, same era.
E. O. Pyzer-Knapp, J. W. Pitera, P. W. Staar, S. Takeda, T. Laino, D. P. Sanders, J. Sexton, J. R. Smith, and A. Curioni, Accelerating materials discovery using artificial intelligence, high performance computing and robotics, npj Computational Materials 8
2022
Cited alongside, same era.
T. Wen, L. Zhang, H. Wang, E. Weinan, and D. J. Srolovitz, Deep potentials for materials science, Materials Futures 1
2022
Cited alongside, same era.
R. Wang, X. Ma, L. Zhang, H. Wang, D. J. Srolovitz, T. Wen, and Z. Wu, Classical and machine learning interatomic potentials for bcc vanadium, Phys. Rev. Mater. 6
2022
Cited alongside, same era.
C. Chen and S. P. Ong, A universal graph deep learning interatomic potential for the periodic table, Nature Computational Science 2
2022
Cited alongside, same era.
J. Ho and T. Salimans, Classifier-free diffusion guidance, arXiv preprint arXiv:2207.12598 (2022)
2022
Cited alongside, same era.
S. Lu, Q. Zhou, X. Chen, Z. Song, and J. Wang, Inverse design with deep generative models: next step in materials discovery, National science review 9
2022
Cited alongside, same era.
D. Raabe, J. R. Mianroodi, and J. Neugebauer, Accelerating the design of compositionally complex materials via physics-informed artificial intelligence, Nature Computational Science 3
2023
Cited alongside, same era.
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 10
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 1
2024
Later among the works it cites.
2024
Later among the works it cites.
S. Liu, T. Wen, A. S. Pattamatta, and D. J. Srolovitz, A prompt-engineered large language model, deep learning workflow for materials classification, Materials Today 80
2024
Later among the works it cites.
2024
Later among the works it cites.
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 , 1 (2025)
2025
Closest in time.