Fetching the paper…
Reading the bibliography…
Pre-trained transformer language models on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequence generation.
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.
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.
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.
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.
Objective-reinforced generative adversarial networks (organ) for sequence generation models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Earlier work this paper cites.
Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
Earlier work this paper cites.
Recent developments in the inorganic crystal structure database: theoretical crystal structure data and related features
Dejan Zagorac, H Müller, S Ruehl, J Zagorac, and Silke Rehme · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon · 2019
Earlier work this paper cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2019
Earlier work this paper cites.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
Cited alongside, same era.
Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 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.
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.
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
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
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.
Molgpt: Molecular generation using a transformer-decoder model
Viraj Bagal, Rishal Aggarwal, PK Vinod, and U Deva Priyakumar · 2021
Later among the works it cites.
C5t5: Controllable generation of organic molecules with transformers
Daniel Rothchild, Alex Tamkin, Julie Yu, Ujval Misra, and Joseph Gonzalez · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Yabo Dan, Yong Zhao, Xiang Li, Shaobo Li, Ming Hu, and Jianjun Hu · 2019
Cited alongside, same era.
Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse design of inorganic materials
Yabo Dan, Yong Zhao, Xiang Li, Shaobo Li, Ming Hu, and Jianjun Hu · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Leveraging pre-trained checkpoints for sequence generation tasks
Sascha Rothe, Shashi Narayan, and Aliaksei Severyn · 2020
Cited alongside, same era.
Progen: Language modeling for protein generation
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R Eguchi, Po-Ssu Huang, and Richard Socher · 2020
Cited alongside, same era.
Signal peptides generated by attention-based neural networks
Zachary Wu, Kevin K Yang, Michael J Liszka, Alycia Lee, Alina Batzilla, David Wernick, David P Weiner, and Frances H Arnold · 2020
Cited alongside, same era.
A generative neural network for maximizing fitness and diversity of synthetic dna and protein sequences
Johannes Linder, Nicholas Bogard, Alexander B Rosenberg, and Georg Seelig · 2020
Cited alongside, same era.
Generative chemical transformer: Neural machine learning of molecular geometric structures from chemical language via attention
Hyunseung Kim, Jonggeol Na, and Won Bo Lee · 2021
Later among the works it cites.
Attention-based generative models for de novo molecular design
Orion Dollar, Nisarg Joshi, David AC Beck, and Jim Pfaendtner · 2021
Later among the works it cites.
How deep learning tools can help protein engineers find good sequences
Margarita Osadchy and Rachel Kolodny · 2021
Later among the works it cites.
Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021
Ben Wang and Aran Komatsuzaki · 2021
Later among the works it cites.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
Later among the works it cites.
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
Later among the works it cites.
Learning to transfer prompts for text generation
Junyi Li, Tianyi Tang, Jian-Yun Nie, Ji-Rong Wen, and Wayne Xin Zhao · 2022
Closest in time.
Rita: a study on scaling up generative protein sequence models
Daniel Hesslow, Niccoló Zanichelli, Pascal Notin, Iacopo Poli, and Debora Marks · 2022
Closest in time.
A deep unsupervised language model for protein design
Noelia Ferruz, Steffen Schmidt, and Birte Höcker · 2022
Closest in time.
Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Edirisuriya Siriwardane, Fanglin Chen, and Jianjun Hu · 2022
Closest in time.
Language models can learn complex molecular distributions
Daniel Flam-Shepherd, Kevin Zhu, and Alán Aspuru-Guzik · 2022
Closest in time.
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 · 2022
Closest in time.
Crystal structure prediction with machine learning-based element substitution
Minoru Kusaba, Chang Liu, and Ryo Yoshida · 2022
Closest in time.
A symmetry-orientated divide-and-conquer method for crystal structure prediction
Xuecheng Shao, Jian Lv, Peng Liu, Sen Shao, Pengyue Gao, Hanyu Liu, Yanchao Wang, and Yanming Ma · 2022
Closest in time.