Machine learning improves metal-organic frameworks design and discovery

被引：3
|
作者：
Tamakloe, Senam
机构：
[1] San Diego, United States
来源：
MRS BULLETIN | 2022年 / 47卷 / 09期
关键词：
Performance material form metal-organic framework (MOF); Performance computing machine learning; Performance computing predictive; Performance computing quantum; quantum information;
D O I：
10.1557/s43577-022-00427-x
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
引用
收藏
页码：886 / 886
页数：1
相关论文
共 50 条
  • [21] Machine learning insights into predicting biogas separation in metal-organic frameworks
    Cooley, Isabel
    Boobier, Samuel
    Hirst, Jonathan D.
    Besley, Elena
    COMMUNICATIONS CHEMISTRY, 2024, 7 (01)
  • [22] Transfer learning for metal-organic frameworks
    Pan, Jie
    NATURE COMPUTATIONAL SCIENCE, 2023, 3 (4): : 280 - 280
  • [23] Interpretable machine learning for materials discovery: Predicting CO2 adsorption properties of metal-organic frameworks
    Teng, Yukun
    Shan, Guangcun
    APL MATERIALS, 2024, 12 (08):
  • [24] Machine Learning-Driven Discovery and Structure-Activity Relationship Analysis of Conductive Metal-Organic Frameworks
    Lin, Jinglong
    Zhang, Huibao
    Asadi, Mojgan
    Zhao, Kai
    Yang, Luming
    Fan, Yunlong
    Zhu, Jintao
    Liu, Qianyi
    Sun, Lei
    Xie, Wen Jun
    Duan, Chenru
    Mo, Fanyang
    Dou, Jin-Hu
    CHEMISTRY OF MATERIALS, 2024, 36 (11) : 5436 - 5445
  • [25] Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
    Aditi S. Krishnapriyan
    Joseph Montoya
    Maciej Haranczyk
    Jens Hummelshøj
    Dmitriy Morozov
    Scientific Reports, 11
  • [26] Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
    Krishnapriyan, Aditi S.
    Montoya, Joseph
    Haranczyk, Maciej
    Hummelshoj, Jens
    Morozov, Dmitriy
    SCIENTIFIC REPORTS, 2021, 11 (01) : 8888
  • [27] Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal-organic frameworks
    Qiao, Zhiwei
    Li, Lifeng
    Li, Shuhua
    Liang, Hong
    Zhou, Jian
    Snurr, Randall Q.
    AICHE JOURNAL, 2021, 67 (10)
  • [28] Machine Learning Enabled Tailor-Made Design of Application-Specific Metal-Organic Frameworks
    Zhang, Xiangyu
    Zhang, Kexin
    Lee, Yongjin
    ACS APPLIED MATERIALS & INTERFACES, 2020, 12 (01) : 734 - 743
  • [29] Enhancing arsenate removal through interpretable machine learning guiding the modular design of metal-organic frameworks
    Lin, Zuhong
    Cai, Hui
    Peng, Hongjia
    Fang, Ying
    Pan, Pengxu
    Li, Haipu
    Yang, Ying
    Yao, Jingjing
    CHEMICAL ENGINEERING JOURNAL, 2024, 497
  • [30] High-throughput and machine learning approaches for the discovery of metal organic frameworks
    Zhang, Xiangyu
    Xu, Zezhao
    Wang, Zidi
    Liu, Huiyu
    Zhao, Yingbo
    Jiang, Shan
    APL MATERIALS, 2023, 11 (06)
12345