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With the rapid advancement of AI technologies, generative models have been increasingly employed in the exploration of novel materials.
Equation of state calculations by fast computing machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E · 1953
Earlier work this paper cites.
Monte carlo sampling methods using markov chains and their applications
Hastings, W. K · 1970
Earlier work this paper cites.
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
Kresse, G. & Furthmüller, J · 1996
Earlier work this paper cites.
Generalized gradient approximation made simple
Perdew, J. P., Burke, K. & Ernzerhof, M · 1996
Earlier work this paper cites.
Maximally localized generalized wannier functions for composite energy bands
Marzari, N. & Vanderbilt, D · 1997
Earlier work this paper cites.
Maximally localized wannier functions for entangled energy bands
Souza, I., Marzari, N. & Vanderbilt, D · 2001
Earlier work this paper cites.
Spin-orbit splittings and energy band gaps calculated with the heyd-scuseria-ernzerhof screened hybrid functional
Peralta, J. E., Heyd, J., Scuseria, G. E. & Martin, R. L · 2006
Earlier work this paper cites.
Topological insulators with inversion symmetry
Fu, L. & Kane, C. L · 2007
Earlier work this paper cites.
Topological insulators in three dimensions
Fu, L., Kane, C. L. & Mele, E. J · 2007
Earlier work this paper cites.
Equivalent expression of z 2 topological invariant for band insulators using the non-abelian berry connection
Yu, R., Qi, X. L., Bernevig, A., Fang, Z. & Dai, X · 2011
Earlier work this paper cites.
Maximally localized wannier functions: Theory and applications
Marzari, N., Mostofi, A. A., Yates, J. R., Souza, I. & Vanderbilt, D · 2012
Earlier work this paper cites.
Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P. et al · 2013
Earlier work this paper cites.
Stochastic surface walking method for structure prediction and pathway searching
Shang, C. & Liu, Z.-P · 2013
Earlier work this paper cites.
Stochastic surface walking method for crystal structure and phase transition pathway prediction
Shang, C., Zhang, X.-J. & Liu, Z.-P · 2014
Earlier work this paper cites.
An updated version of wannier90: A tool for obtaining maximally-localised wannier functions
Mostofi, A. A. et al · 2014
Earlier work this paper cites.
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Agrawal, A. & Choudhary, A · 2016
Earlier work this paper cites.
Topological quantum chemistry
Bradlyn, B. et al · 2017
Earlier work this paper cites.
The atomic simulation environment—a python library for working with atoms
Larsen, A. H. et al · 2017
Earlier work this paper cites.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie, T. & Grossman, J. C · 2018
Earlier work this paper cites.
Wanniertools: An open-source software package for novel topological materials
Wu, Q., Zhang, S., Song, H.-F., Troyer, M. & Soluyanov, A. A · 2018
Earlier work this paper cites.
Graph networks as a universal machine learning framework for molecules and crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P · 2019
Earlier work this paper cites.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Gebauer, N., Gastegger, M. & Schütt, K · 2019
Earlier work this paper cites.
Cgmh: Constrained sentence generation by metropolis-hastings sampling
Miao, N., Zhou, H., Mou, L., Yan, R. & Li, L · 2019
Earlier work this paper cites.
Symtopo: An automatic tool for calculating topological properties of nonmagnetic crystalline materials
He, Y. et al · 2019
Earlier work this paper cites.
A complete catalogue of high-quality topological materials
Vergniory, M. et al · 2019
Earlier work this paper cites.
Catalogue of topological electronic materials
Zhang, T. et al · 2019
Earlier work this paper cites.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Gasteiger, J., Giri, S., Margraf, J. T. & Günnemann, S · 2020
Earlier work this paper cites.
Zhang, M., Jiang, N., Li, L. & Xue, Y · 2020
Earlier work this paper cites.
Dealing with the foreign-body response to implanted biomaterials: strategies and applications of new materials
Zhang, D. et al · 2021
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Research directions for next-generation battery management solutions in automotive applications
Hu, X., Deng, Z., Lin, X., Xie, Y. & Teodorescu, R · 2021
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Engineering crystal structures with light
Disa, A. S., Nova, T. F. & Cavalleri, A · 2021
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Recent advances in photonic crystal optical devices: A review
Butt, M., Khonina, S. N. & Kazanskiy, N · 2021
Cited alongside, same era.
Atomistic line graph neural network for improved materials property predictions
Choudhary, K. & DeCost, B · 2021
Cited alongside, same era.
Gemnet: Universal directional graph neural networks for molecules
Gasteiger, J., Becker, F. & Günnemann, S · 2021
A cluster-based deep learning model perceiving series correlation for accurate prediction of phonon spectrum
Liang, C. et al · 2024
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Mattersim: A deep learning atomistic model across elements, temperatures and pressures
Yang, H. et al · 2024
Later among the works it cites.
Open materials 2024 (omat24) inorganic materials dataset and models
Barroso-Luque, L. et al · 2024
Later among the works it cites.
Universal machine learning kohn–sham hamiltonian for materials
Zhong, Y. et al · 2024
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Wang, Y. et al · 2024
Later among the works it cites.
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Crystal diffusion variational autoencoder for periodic material generation
Xie, T., Fu, X., Ganea, O.-E., Barzilay, R. & Jaakkola, T · 2021
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Vaspkit: A user-friendly interface facilitating high-throughput computing and analysis using vasp code
Wang, V., Xu, N., Liu, J.-C., Tang, G. & Geng, W.-T · 2021
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Advances in photonic crystal fiber-based sensor for detection of physical and biochemical parameters—a review
Chaudhary, V. S., Kumar, D., Pandey, B. P. & Kumar, S · 2022
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Machine learning accelerates the materials discovery
Fang, J. et al · 2022
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A universal graph deep learning interatomic potential for the periodic table
Chen, C. & Ong, S. P · 2022
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Designing high-tc superconductors with bcs-inspired screening, density functional theory, and deep-learning
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Space group constrained crystal generation
Jiao, R., Huang, W., Liu, Y., Zhao, D. & Liu, Y · 2024
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Space group informed transformer for crystalline materials generation
Cao, Z., Luo, X., Lv, J. & Wang, L · 2024
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Flowmm: Generating materials with riemannian flow matching
Miller, B. K., Chen, R. T., Sriram, A. & Wood, B. M · 2024
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Crystalflow: A flow-based generative model for crystalline materials
Luo, X. et al · 2024
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Vqcrystal: Leveraging vector quantization for discovery of stable crystal structures
Qiu, Z. et al · 2024
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Flowllm: Flow matching for material generation with large language models as base distributions
Sriram, A., Miller, B., Chen, R. T. & Wood, B · 2024
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Mattergpt: A generative transformer for multi-property inverse design of solid-state materials
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Con-cdvae: A method for the conditional generation of crystal structures
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Deep learning generative model for crystal structure prediction
Luo, X. et al · 2024
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Crystalline material discovery in the era of artificial intelligence (2025)
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A generative model for inorganic materials design
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Design topological materials by reinforcement fine-tuned generative model (2025)
Xu, H., Qian, D., Liu, Z., Jiang, Y. & Wang, J · 2025
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Li, Z., Liu, S., Ye, B., Srolovitz, D. J. & Wen, T · 2025
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Invdesflow: An ai-driven materials inverse design workflow to explore possible high-temperature superconductors
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Discovery of new topological insulators and semimetals using deep generative models
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