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Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic molecules.
ab initio
G. Kresse and J. Hafner · 1993
Earlier work this paper cites.
ab initio
G. Kresse and J. Hafner · 1994
Earlier work this paper cites.
Projector augmented-wave method
P. E. Blöchl · 1994
Earlier work this paper cites.
Transition metal-based double nitrides
S Kikkawa, T Yamamoto, K Ohta, M Takahashi, and F Kanamaru · 1996
Earlier work this paper cites.
Efficiency of ab initio total energy calculations for metals and semiconductors using a plane-wave basis set
J. Furthmüller G. Kresse · 1996
Earlier work this paper cites.
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
G. Kresse and J. Furthmüller · 1996
Earlier work this paper cites.
Generalized gradient approximation made simple
John P. Perdew, Kieron Burke, and Matthias Ernzerhof · 1996
Earlier work this paper cites.
Generalized gradient approximation made simple [phys. rev. lett. 77, 3865 (1996)]
John P. Perdew, Kieron Burke, and Matthias Ernzerhof · 1997
Earlier work this paper cites.
From ultrasoft pseudopotentials to the projector augmented-wave method
G. Kresse and D. Joubert · 1999
Earlier work this paper cites.
Bond graph representation and gp for automated analog filter design
Zhun Fan, Jianjun Hu, Kisung Seo, E Goodman, R Rosenberg, and Baihai Zhang · 2001
Earlier work this paper cites.
Superconductivity in doped cubic silicon
Etienne Bustarret, C Marcenat, P Achatz, J Kačmarčik, F Lévy, A Huxley, L Ortéga, E Bourgeois, Xavier Blase, D Débarre, et al · 2006
Earlier work this paper cites.
Regulation of organ growth by morphogen gradients
Gerald Schwank and Konrad Basler · 2010
Earlier work this paper cites.
Data mined ionic substitutions for the discovery of new compounds
Geoffroy Hautier, Chris Fischer, Virginie Ehrlacher, Anubhav Jain, and Gerbrand Ceder · 2011
Earlier work this paper cites.
Synthesis and characterization of mo-doped srfeo3- δ \delta as cathode materials for solid oxide fuel cells
Guoliang Xiao, Qiang Liu, Siwei Wang, Vasileios G Komvokis, Michael D Amiridis, Andreas Heyden, Shuguo Ma, and Fanglin Chen · 2012
Earlier work this paper cites.
Electrochemical behavior of cr-doped composite li2mno3-limn0. 5ni0. 5o2 cathode materials
Gurpreet Singh, R Thomas, Arun Kumar, and RS Katiyar · 2012
Earlier work this paper cites.
Crystal structure prediction using the uspex code
AR Oganov, Andriy Lyakhov, Mario Valle, and Gilles Frapper · 2012
Earlier work this paper cites.
Commentary: The materials project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al · 2013
Earlier work this paper cites.
Bismuth doped lanthanum ferrite perovskites as novel cathodes for intermediate-temperature solid oxide fuel cells
Mei Li, Yao Wang, Yunlong Wang, Fanglin Chen, and Changrong Xia · 2014
Earlier work this paper cites.
Electrochemical performance and thermal stability of li 1.18 co 0.15 ni 0.15 mn 0.52 o 2 surface coated with the ionic conductor li 3 vo 4
Qiang Fu, Fei Du, Xiaofei Bian, Yuhui Wang, Xiao Yan, Yongquan Zhang, Kai Zhu, Gang Chen, Chunzhong Wang, and Yingjin Wei · 2014
Earlier work this paper cites.
Li-ion battery materials: present and future
Naoki Nitta, Feixiang Wu, Jung Tae Lee, and Gleb Yushin · 2015
Earlier work this paper cites.
Computational screening of all stoichiometric inorganic materials
Daniel W Davies, Keith T Butler, Adam J Jackson, Andrew Morris, Jarvist M Frost, Jonathan M Skelton, and Aron Walsh · 2016
Earlier work this paper cites.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
Earlier work this paper cites.
Learning atoms for materials discovery
Quan Zhou, Peizhe Tang, Shenxiu Liu, Jinbo Pan, Qimin Yan, and Shou-Cheng Zhang · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Enhanced co 2 electrolysis with a srtio 3 cathode through a dual doping strategy
Lingting Ye, Xiuli Hu, Xin Wang, Fanglin Chen, Dian Tang, Dehua Dong, and Kui Xie · 2019
Cited alongside, same era.
Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
Cited alongside, same era.
Unified rational protein engineering with sequence-based deep representation learning
Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M Church · 2019
Cited alongside, same era.
Unsupervised word embeddings capture latent knowledge from materials science literature
Vahe Tshitoyan, John Dagdelen, Leigh Weston, Alexander Dunn, Ziqin Rong, Olga Kononova, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2019
Doping-induced quantum spin hall insulator to superconductor transition
Zhenjiu Wang, Yuhai Liu, Toshihiro Sato, Martin Hohenadler, Chong Wang, Wenan Guo, and Fakher F Assaad · 2021
Later among the works it cites.
Heterogeneous catalysts in grammar school
Johannes T Margraf, Zachary W Ulissi, Yousung Jung, and Karsten Reuter · 2021
Later among the works it cites.
Frequency effects on syntactic rule learning in transformers
Jason Wei, Dan Garrette, Tal Linzen, and Ellie Pavlick · 2021
Later among the works it cites.
Pretrained language models for text generation: A survey
Junyi Li, Tianyi Tang, Wayne Xin Zhao, and Ji-Rong Wen · 2021
Later among the works it cites.
Proteinbert: A universal deep-learning model of protein sequence and function
Nadav Brandes, Dan Ofer, Yam Peleg, Nadav Rappoport, and Michal Linial · 2021
Later among the works it cites.
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
A map of the inorganic ternary metal nitrides
Wenhao Sun, Christopher J Bartel, Elisabetta Arca, Sage R Bauers, Bethany Matthews, Bernardo Orvañanos, Bor-Rong Chen, Michael F Toney, Laura T Schelhas, William Tumas, et al · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
Cited alongside, same era.
Sr3crn3: A new electride with a partially filled d-shell transition metal
Padtaraporn Chanhom, Kevin E Fritz, Lee A Burton, Jan Kloppenburg, Yaroslav Filinchuk, Anatoliy Senyshyn, Maoyu Wang, Zhenxing Feng, Numpon Insin, Jin Suntivich, et al · 2019
Cited alongside, same era.
Smact: Semiconducting materials by analogy and chemical theory
Daniel W Davies, Keith T Butler, Adam J Jackson, Jonathan M Skelton, Kazuki Morita, and Aron Walsh · 2019
Cited alongside, same era.
Machine learning in materials discovery: confirmed predictions and their underlying approaches
James E Saal, Anton O Oliynyk, and Bryce Meredig · 2020
Cited alongside, same era.
Bosheng Song, Zimeng Li, Xuan Lin, Jianmin Wang, Tian Wang, and Xiangzheng Fu · 2021
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Review of unsupervised pretraining strategies for molecules representation
Linhui Yu, Yansen Su, Yuansheng Liu, and Xiangxiang Zeng · 2021
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Extracting predictive representations from hundreds of millions of molecules
Dong Chen, Jiaxin Zheng, Guo-Wei Wei, and Feng Pan · 2021
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Mg-bert: leveraging unsupervised atomic representation learning for molecular property prediction
Xiao-Chen Zhang, Cheng-Kun Wu, Zhi-Jiang Yang, Zhen-Xing Wu, Jia-Cai Yi, Chang-Yu Hsieh, Ting-Jun Hou, and Dong-Sheng Cao · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Lawrence Zitnick, Jerry Ma, et al · 2021
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Molgpt: Molecular generation using a transformer-decoder model
Viraj Bagal, Rishal Aggarwal, PK Vinod, and U Deva Priyakumar · 2021
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C5t5: Controllable generation of organic molecules with transformers
Daniel Rothchild, Alex Tamkin, Julie Yu, Ujval Misra, and Joseph Gonzalez · 2021
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Generative chemical transformer: Neural machine learning of molecular geometric structures from chemical language via attention
Hyunseung Kim, Jonggeol Na, and Won Bo Lee · 2021
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Attention-based generative models for de novo molecular design
Orion Dollar, Nisarg Joshi, David AC Beck, and Jim Pfaendtner · 2021
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Jianjun Hu, Stanislav Stefanov, Yuqi Song, Sadman Sadeed Omee, Steph-Yves Louis, Edirisuriya Siriwardane, and Yong Zhao · 2021
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Tcsp: a template based crystal structure prediction algorithm and web server for materials discovery
Lai Wei, Nihang Fu, Edirisuriya Siriwardane, Wenhui Yang, Sadman Sadeed Omee, Rongzhi Dong, Rui Xin, and Jianjun Hu · 2021
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Scalable deeper graph neural networks for high-performance materials property prediction
Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, and Jianjun Hu · 2021
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Alphacrystal: Contact map based crystal structure prediction using deep learning
Jianjun Hu, Yong Zhao, Yuqi Song, Rongzhi Dong, Wenhui Yang, Yuxin Li, and Edirisuriya Siriwardane · 2021
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Netsolp: predicting protein solubility in escherichia coli using language models
Vineet Thumuluri, Hannah-Marie Martiny, Jose J Almagro Armenteros, Jesper Salomon, Henrik Nielsen, and Alexander Rosenberg Johansen · 2022
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Proteinbert: A universal deep-learning model of protein sequence and function
N Brandes, D Ofer, Y Peleg, N Rappoport, and M Linial · 2022
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Crystal structure prediction with machine learning-based element substitution
Minoru Kusaba, Chang Liu, and Ryo Yoshida · 2022
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A symmetry-orientated divide-and-conquer method for crystal structure prediction
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