Materials Transformers Language Models for Generative Materials Design: a benchmark study

27 June 2022

Papers citing "Materials Transformers Language Models for Generative Materials Design: a benchmark study"

8 / 8 papers shown

Title
Bayesian Optimization of Catalysis With In-Context Learning M. C. Ramos Shane S. Michtavy Marc D. Porosoff Andrew D. White BDL 40 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 14 1 0 29 Nov 2022
Keeping it Simple: Language Models can learn Complex Molecular Distributions Daniel Flam-Shepherd Kevin Zhu A. Aspuru‐Guzik 122 142 0 06 Dec 2021
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 68 73 0 25 Sep 2021
Frequency Effects on Syntactic Rule Learning in Transformers Jason W. Wei Dan Garrette Tal Linzen Ellie Pavlick 80 62 0 14 Sep 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling Leo Gao Stella Biderman Sid Black Laurence Golding Travis Hoppe ... Horace He Anish Thite Noa Nabeshima Shawn Presser Connor Leahy AIMat 248 1,986 0 31 Dec 2020
Blank Language Models T. Shen Victor Quach Regina Barzilay Tommi Jaakkola 201 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 169 628 0 29 Nov 2018