Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials

25 April 2022

Papers citing "Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials"

7 / 7 papers shown

Title
Bayesian Optimization of Catalysis With In-Context Learning M. C. Ramos Shane S. Michtavy Marc D. Porosoff Andrew D. White BDL 44 30 0 11 Apr 2023
Composition based oxidation state prediction of materials using deep learning Nihang Fu Jeffrey Hu Yingqi Feng G. Morrison H. Loye Jianjun Hu 33 1 0 29 Nov 2022
Probabilistic Generative Transformer Language models for Generative Design of Molecules Lai Wei Nihang Fu Yuqi Song Qian Wang Jianjun Hu AI4CE 33 11 0 20 Sep 2022
Scalable deeper graph neural networks for high-performance materials property prediction Sadman Sadeed Omee Steph-Yves M. Louis Nihang Fu Lai Wei Sourin Dey Rongzhi Dong Qinyang Li Jianjun Hu 70 73 0 25 Sep 2021
Frequency Effects on Syntactic Rule Learning in Transformers Jason W. Wei Dan Garrette Tal Linzen Ellie Pavlick 88 62 0 14 Sep 2021
Blank Language Models T. Shen Victor Quach Regina Barzilay Tommi Jaakkola 203 73 0 08 Feb 2020
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models Daniil Polykovskiy Alexander Zhebrak Benjamín Sánchez-Lengeling Sergey Golovanov Oktai Tatanov ... Simon Johansson Hongming Chen Sergey I. Nikolenko Alán Aspuru-Guzik Alex Zhavoronkov ELM 194 633 0 29 Nov 2018