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Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space.
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G. & Furthmüller, J · 1996
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Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
Kresse, G. & Furthmüller, J · 1996
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From the computer to the laboratory: materials discovery and design using first-principles calculations
Hautier, G., Jain, A. & Ong, S. P · 2012
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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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Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P. et al · 2013
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What is high-throughput virtual screening? a perspective from organic materials discovery
Pyzer-Knapp, E. O., Suh, C., Gómez-Bombarelli, R., Aguilera-Iparraguirre, J. & Aspuru-Guzik, A · 2015
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Computational screening of all stoichiometric inorganic materials
Davies, D. W. et al · 2016
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Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W. & Shi, S · 2017
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Attention is all you need
Vaswani, A. et al · 2017
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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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Data-driven approach to encoding and decoding 3-d crystal structures
Hoffmann, J. et al · 2019
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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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Solar materials find their band gap
Sutherland, B. R · 2020
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Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
Dunn, A., Wang, Q., Ganose, A., Dopp, D. & Jain, A · 2020
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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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Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
Long, T. et al · 2021
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Benchmarking graph neural networks for materials chemistry
Fung, V., Zhang, J., Juarez, E. & Sumpter, B. G · 2021
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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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An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Ren, Z. et al · 2022
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Atomic structure generation from reconstructing structural fingerprints
Fung, V. et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
The impact of large language models on scientific discovery: a preliminary study using gpt-4
AI4Science, M. R. & Quantum, M. A · 2023
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Catalyst energy prediction with catberta: Unveiling feature exploration strategies through large language models
Ock, J., Guntuboina, C. & Barati Farimani, A · 2023
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Flam-Shepherd, D. & Aspuru-Guzik, A · 2023
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Crystal structure generation with autoregressive large language modeling
Antunes, L. M., Butler, K. T. & Grau-Crespo, R · 2023
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Wei, J. et al · 2022
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Towards reasoning in large language models: A survey
Huang, J. & Chang, K. C.-C · 2022
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Pre-trained language models for interactive decision-making
Li, S. et al · 2022
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Do as i can, not as i say: Grounding language in robotic affordances
Ahn, M. et al · 2022
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Torchmd-net: Equivariant transformers for neural network based molecular potentials
Thölke, P. & De Fabritiis, G · 2022
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Scaling deep learning for materials discovery
Merchant, A. et al · 2023
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Mattergen: a generative model for inorganic materials design
Zeni, C. et al · 2023
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Achiam, J. et al · 2023
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Gemini: a family of highly capable multimodal models
Team, G. et al · 2023
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Recent advances and outstanding challenges for machine learning interatomic potentials
Ko, T. W. & Ong, S. P · 2023
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Are large language models superhuman chemists?
Mirza, A. et al · 2024
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Openchemie: An information extraction toolkit for chemistry literature
Fan, V. et al · 2024
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Extracting structured data from organic synthesis procedures using a fine-tuned large language model
Ai, Q., Meng, F., Shi, J., Pelkie, B. & Coley, C. W · 2024
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Benchmarking large language models for molecule prediction tasks
Zhong, Z., Zhou, K. & Mottin, D · 2024
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Fine-tuning gpt-3 for machine learning electronic and functional properties of organic molecules
Xie, Z. et al · 2024
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Leveraging large language models for predictive chemistry
Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A. & Smit, B · 2024
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Fine-tuned language models generate stable inorganic materials as text
Gruver, N. et al · 2024
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Derivative-based pre-training of graph neural networks for materials property predictions
Jia, S. et al · 2024
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