De novo design of polymer electrolytes using GPT-based and diffusion-based generative models  

作　　者：Zhenze Yang Weike Ye Xiangyun Lei Daniel Schweigert Ha-Kyung Kwon Arash Khajeh 

机构地区：[1]Toyota Research Institute,Los Altos,CA,USA [2]Department of Materials Science and Engineering,Massachusetts Institute of Technology,Cambridge,MA,USA

出　　处：《npj Computational Materials》2024年第1期119-131,共13页计算材料学(英文)

摘　　要：Solid polymer electrolytes offer promising advancements for next-generation batteries,boasting superior safety,enhanced specific energy,and extended lifespans over liquid electrolytes.However,low ionic conductivity and the vast polymer space hinder commercialization.This study leverages generative AI for de novo polymer electrolyte design,comparing GPT-based and diffusion-based models with extensive hyperparameter tuning.We evaluate these models using various metrics and full-atom molecular dynamics simulations.Among 46 candidates tested,17 exhibit superior ionic conductivity,surpassing existing polymers in our database,with some doubling the conductivity values.Additionally,by adopting pretraining and fine-tuning methodologies,we significantly enhance our generative models,achieving quicker convergence,better performance with limited data,and greater diversity.Our method efficiently generates a large number of novel,diverse,and valid polymers,with a high likelihood of synthesizability,enabling the identification of promising candidates with markedly improved efficiency and effectiveness for practical applications.

关 键 词：polymer DIFFUSION CONDUCTIVITY 

分 类 号：O63[理学—高分子化学]