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Deep learning-based generative models have emerged as powerful tools for modeling complex data distributions and generating high-fidelity samples, offering a transformative approach to efficiently explore the configuration space of crystalline materials.
Projector augmented-wave method
Blöchl, P. E · 1994
Earlier work this paper cites.
Efficient iterative schemes for ab initio total-energy calculations 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.
Exponential multiplicity of inherent structures
Stillinger, F. H · 1999
Earlier work this paper cites.
Designing a New Material World
Olson, G. B · 2000
Earlier work this paper cites.
Minima hopping: An efficient search method for the global minimum of the potential energy surface of complex molecular systems
Goedecker, S · 2004
Earlier work this paper cites.
USPEX—Evolutionary crystal structure prediction
Glass, C. W., Oganov, A. R. & Hansen, N · 2006
Earlier work this paper cites.
Crystal structure prediction via particle-swarm optimization
Wang, Y., Lv, J., Zhu, L. & Ma, Y · 2010
Earlier work this paper cites.
Ab initio random structure searching
Pickard, C. J. & Needs, R. J · 2011
Earlier work this paper cites.
Superconductive sodalite-like clathrate calcium hydride at high pressures
Wang, H., Tse, J. S., Tanaka, K., Iitaka, T. & Ma, Y · 2012
Earlier work this paper cites.
CALYPSO: A method for crystal structure prediction
Wang, Y., Lv, J., Zhu, L. & Ma, Y · 2012
Earlier work this paper cites.
Symmetrisation schemes for global optimisation of atomic clusters
Oakley, M. T., Johnston, R. L. & Wales, D. J · 2013
Earlier work this paper cites.
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A. et al · 2013
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.
Perspective: Crystal structure prediction at high pressures
Wang, Y. & Ma, Y · 2014
Earlier work this paper cites.
A general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A. & Wolverton, C · 2016
Earlier work this paper cites.
Potential high-Tc superconducting lanthanum and yttrium hydrides at high pressure
Liu, H., Naumov, I. I., Hoffmann, R., Ashcroft, N. W. & Hemley, R. J · 2017
Earlier work this paper cites.
Hydrogen Clathrate Structures in Rare Earth Hydrides at High Pressures: Possible Route to Room-Temperature Superconductivity
Peng, F. et al · 2017
Earlier work this paper cites.
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
Nouira, A., Sokolovska, N. & Crivello, J.-C · 2018
Earlier work this paper cites.
Neural Ordinary Differential Equations
Chen, R. T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D · 2018
Earlier work this paper cites.
Structure prediction drives materials discovery
Oganov, A. R., Pickard, C. J., Zhu, Q. & Needs, R. J · 2019
Earlier work this paper cites.
Superconductivity at 250 K in lanthanum hydride under high pressures
Drozdov, A. P. et al · 2019
Earlier work this paper cites.
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
Hoffmann, J. et al · 2019
Cited alongside, same era.
Inverse Design of Solid-State Materials via a Continuous Representation
Noh, J. et al · 2019
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SMACT: Semiconducting Materials by Analogy and Chemical Theory
Davies, D. et al · 2019
Cited alongside, same era.
Language Models are Few-Shot Learners
Brown, T. B. et al · 2020
Cited alongside, same era.
3‑D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning
Court, C. J., Yildirim, B., Jain, A. & Cole, J. M · 2020
Cited alongside, same era.
Improving and generalizing flow-based generative models with minibatch optimal transport
Tong, A. et al · 2023
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Guided Flows for Generative Modeling and Decision Making
Zheng, Q. et al · 2023
Later among the works it cites.
Building Normalizing Flows with Stochastic Interpolants
Albergo, M. S. & Vanden-Eijnden, E · 2023
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Klein, L., Krämer, A. & Noé, F · 2023
Later among the works it cites.
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
Deng, B. et al · 2023
Later among the works it cites.
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Kim, B., Lee, S. & Kim, J · 2020
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Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Köhler, J., Klein, L. & Noé, F · 2020
Cited alongside, same era.
Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity
Zimmermann, N. E. R. & Jain, A · 2020
Cited alongside, same era.
Highly accurate protein structure prediction with AlphaFold
Jumper, J. et al · 2021
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High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B · 2022
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An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Ren, Z. et al · 2022
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Con-CDVAE: A method for the conditional generation of crystal structures
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