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Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength.
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An introduction to the theory of piezoelectricity , volume 9
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Bilbao crystallographic server: I. databases and crystallographic computing programs
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AFLOW: An automatic framework for high-throughput materials discovery
Curtarolo, S., Setyawan, W., Hart, G. L., Jahnatek, M., Chepulskii, R. V., Taylor, R. H., Wang, S., Xue, J., Yang, K., Levy, O., Mehl, M. J., Stokes, H. T., Demchenko, D. O., and Morgan, D · 2012
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On representing chemical environments
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The high-throughput highway to computational materials design
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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
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Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
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Symmetry and physical properties of crystals
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Attention is all you need
Vaswani, A · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J · 2018
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Atomic-position independent descriptor for machine learning of material properties
Jain, A. and Bligaard, T · 2018
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Compositional optimization of hard-magnetic phases with machine-learning models
Möller, J. J., Körner, W., Krugel, G., Urban, D. F., and Elsässer, C · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie, T. and Grossman, J. C · 2018
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Smact: Semiconducting materials by analogy and chemical theory
Davies, D. W., Butler, K. T., Jackson, A. J., Skelton, J. M., Morita, K., and Walsh, A · 2019
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Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
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Wyckoff set regression for materials discovery
Goodall, R. E., Parackal, A. S., Faber, F. A., and Armiento, R · 2020
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3DSC-a dataset of superconductors including crystal structures
Sommer, T., Willa, R., Schmalian, J., and Friederich, P · 2023
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Swallowing the bitter pill: Simplified scalable conformer generation
Wang, Y., Elhag, A. A., Jaitly, N., Susskind, J. M., and Bautista, M. Á · 2023
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Scalable diffusion for materials generation, 2023
Yang, M., Cho, K., Merchant, A., Abbeel, P., Schuurmans, D., Mordatch, I., and Cubuk, E. D · 2023
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Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al · 2024
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Spglib: a software library for crystal symmetry search
Atsushi Togo, K. S. and Tanaka, I · 2024
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Fredericks, S., Parrish, K., Sayre, D., and Zhu, Q · 2021
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AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes
Hicks, D., Toher, C., Ford, D. C., Rose, F., Santo, C. D., Levy, O., Mehl, M. J., and Curtarolo, S · 2021
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Compositionally restricted attention-based network for materials property predictions
Wang, A. Y.-T., Kauwe, S. K., Murdock, R. J., and Sparks, T. D · 2021
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Crystal diffusion variational autoencoder for periodic material generation
Xie, T., Fu, X., Ganea, O.-E., Barzilay, R., and Jaakkola, T · 2021
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Crystal symmetry based selection rules for anharmonic phonon-phonon scattering from a group theory formalism
Yang, R., Yue, S., Quan, Y., and Liao, B · 2021
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Rapid discovery of stable materials by coordinate-free coarse graining
Goodall, R. E., Parackal, A. S., Faber, F. A., Armiento, R., and Lee, A. A · 2022
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Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Pyzer-Knapp, E. O., Pitera, J. W., Staar, P. W., Takeda, S., Laino, T., Sanders, D. P., Sexton, J., Smith, J. R., and Curioni, A · 2022
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matbench-genmetrics: A Python library for benchmarking crystal structure generative models using time-based splits of Materials Project structures
Baird, S. G., Sayeed, H. M., Montoya, J., and Sparks, T. D · 2024
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Space group informed transformer for crystalline materials generation
Cao, Z., Luo, X., Lv, J., and Wang, L · 2024
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Fine-tuned language models generate stable inorganic materials as text
Gruver, N., Sriram, A., Madotto, A., Wilson, A. G., Zitnick, C. L., and Ulissi, Z · 2024
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SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Levy, D., Panigrahi, S. S., Kaba, S.-O., Zhu, Q., Galkin, M., Miret, S., and Ravanbakhsh, S · 2024
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Deep learning generative model for crystal structure prediction
Luo, X., Wang, Z., Gao, P., Lv, J., Wang, Y., Chen, C., and Ma, Y · 2024
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FlowMM: Generating Materials with Riemannian Flow Matching
Miller, B. K., Chen, R. T., Sriram, A., and Wood, B. M · 2024
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CrySPR: A Python interface for implementation of crystal structure pre-relaxation and prediction using machine-learning interatomic potentials
Nong, W., Zhu, R., and Hippalgaonkar, K · 2024
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Representation-space diffusion models for generating periodic materials
Sinha, A., Jia, S., and Fung, V · 2024
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Matgpt: A vane of materials informatics from past, present, to future
Wang, Z., Chen, A., Tao, K., Han, Y., and Li, J · 2024
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WyCryst: Wyckoff inorganic crystal generator framework
Zhu, R., Nong, W., Yamazaki, S., and Hippalgaonkar, K · 2024
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Atomate2: Modular Workflows for Materials Science
Ganose, A. M., Sahasrabuddhe, H., Asta, M., Beck, K., Biswas, T., Bonkowski, A., Bustamante, J., Chen, X., Chiang, Y., Chrzan, D., Clary, J., Cohen, O., Ertural, C., Gallant, M., George, J., Gerits, S., Goodall, R., Guha, R., Hautier, G., Horton, M., Kaplan, A., Kingsbury, R., Kuner, M., Li, B., Linn, X., McDermott, M., Mohanakrishnan, R. S., Naik, A., Neaton, J., Persson, K., Petretto, G., Purcell, T., Ricci, F., Rich, B., Riebesell, J., Rignanese, G.-M., Rosen, A., Scheffler, M., Schmidt, J., Shen, J.-X., Sobolev, A., Sundararaman, R., Tezak, C., Trinquet, V., Varley, J., Vigil-Fowler, D., Wang, D., Waroquiers, D., Wen, M., Yang, H., Zheng, H., Zheng, J., Zhu, Z., and Jain, A · 2025
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Yamazaki, S., Nong, W., Zhu, R., Novoselov, K. S., Ustyuzhanin, A., and Hippalgaonkar, K · 2025
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A generative model for inorganic materials design
Zeni, C., Pinsler, R., Zügner, D., Fowler, A., Horton, M., Fu, X., Wang, Z., Shysheya, A., Crabbé, J., Ueda, S., Sordillo, R., Sun, L., Smith, J., Nguyen, B., Schulz, H., Lewis, S., Huang, C.-W., Lu, Z., Zhou, Y., Yang, H., Hao, H., Li, J., Yang, C., Li, W., Tomioka, R., and Xie, T · 2025
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