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The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition.
A systematic method of deriving new semiconducting compounds by structural analogy
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Über Alkali-oxofluorometallate der Übergangsmetalle. A ′ 3 \text{A\text{${}^{\prime}$}}{\vphantom{\text{X}}}_{\vphantom{\text{2}}\smash[t]{\text{3}}}^{\vphantom{\smash[t]{\text{2}}}\hphantom{\text{3}}\text{}} MeO x F 6-x -Verbindungen mit x = 1, 2, 3
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High-Pressure Phases of Silane
Pickard, C. J. & Needs, R · 2006
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Crystal structure prediction using ab initio
Oganov, A. R. & Glass, C. W · 2006
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Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
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Multi-armed Bandits with Episode Context
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New cubic perovskites for one- and two-photonwater splitting using the computational materials repository
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Computational screening of perovskite metal oxides for optimal solar light capture
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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A. et al · 2013
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Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C · 2013
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Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
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Generative Adversarial Nets
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Identification of Novel Cu, Ag, and Au Ternary Oxides from Global Structural Prediction
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Computational Screening of All Stoichiometric Inorganic Materials
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Attention Is All You Need
Vaswani, A. et al · 2017
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RandSpg: An open-source program for generating atomistic crystal structures with specific spacegroups
Avery, P. & Zurek, E · 2017
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Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A · 2018
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Improving Language Understanding by Generative Pre-Training (2018)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I. et al · 2018
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Reinforcement Learning: An Introduction (MIT press, 2018)
Sutton, R. S. & Barto, A. G · 2018
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Generating Wikipedia by Summarizing Long Sequences
Liu, P. J. et al · 2018
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Spglib: a software library for crystal symmetry search
Togo, A. & Tanaka, I · 2018
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Structure prediction drives materials discovery
Oganov, A. R., Pickard, C. J., Zhu, Q. & Needs, R. J · 2019
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Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
Podryabinkin, E. V., Tikhonov, E. V., Shapeev, A. V. & Oganov, A. R · 2019
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
Kang, S. 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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Novel inorganic crystal structures predicted using autonomous simulation agents
Ye, W., Lei, X., Aykol, M. & Montoya, J. H · 2022
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Machine Learning Approaches for Accelerating the Discovery of Thermoelectric Materials
Antunes, L. M. et al · 2022
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Structure prediction and materials design with generative neural networks
Yan, D., Smith, A. D. & Chen, C.-C · 2023
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Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P · 2019
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Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features
Zagorac, D., Müller, H., Ruehl, S., Zagorac, J. & Rehme, S · 2019
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Fine-Tuning Language Models from Human Preferences
Ziegler, D. M. et al · 2019
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The NOMAD laboratory: from data sharing to artificial intelligence
Draxl, C. & Scheffler, M · 2019
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Computational Investigation of Copper Phosphides as Conversion Anodes for Lithium-Ion Batteries
Harper, A. F., Evans, M. L. & Morris, A. J · 2020
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3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning
Court, C. J., Yildirim, B., Jain, A. & Cole, J. M · 2020
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Cao, Y. et al · 2023
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https://openai.com/blog/chatgpt
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Bran, A. M., Cox, S., White, A. D. & Schwaller, P · 2023
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Is GPT-3 all you need for low-data discovery in chemistry?
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Autonomous chemical research with large language models
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Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task
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https://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 License · 2023
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Generative adversarial networks and diffusion models in material discovery
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mp-time-split
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