Modeling of the hydration of nanoporous materials by machine learning

被引：0
|
作者：
Bauchy, Mathieu [1 ]
机构：
[1] Univ Calif Los Angeles, Los Angeles, CA USA
来源：
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY | 2019年 / 258卷
关键词：
D O I：
暂无
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
671-COLL
引用
收藏
页数：1
相关论文
共 50 条
  • [1] Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials
    Korolev, Vadim V.
    Mitrofanov, Artem
    Marchenko, Ekaterina, I
    Eremin, Nickolay N.
    Tkachenko, Valery
    Kalmykov, Stepan N.
    CHEMISTRY OF MATERIALS, 2020, 32 (18) : 7822 - 7831
  • [2] Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials
    Vishnyakov, Aleksey
    MATERIALS, 2025, 18 (03)
  • [3] A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials
    Fanourgakis, George S.
    Gkagkas, Konstantinos
    Tylianakis, Emmanuel
    Klontzas, Emmanuel
    Froudakis, George
    JOURNAL OF PHYSICAL CHEMISTRY A, 2019, 123 (28): : 6080 - 6087
  • [4] A Generic Machine Learning Algorithm for the Prediction of Gas Adsorption in Nanoporous Materials
    Fanourgakis, George S.
    Gkagkas, Konstantinos
    Tylianakis, Emmanuel
    Froudakis, George
    JOURNAL OF PHYSICAL CHEMISTRY C, 2020, 124 (13): : 7117 - 7126
  • [5] Machine learning unifies the modeling of materials and molecules
    Bartok, Albert P.
    De, Sandip
    Poelking, Carl
    Bernstein, Noam
    Kermode, James R.
    Csanyi, Gabor
    Ceriotti, Michele
    SCIENCE ADVANCES, 2017, 3 (12):
  • [6] Machine learning modeling for the prediction of materials energy
    Meriem Mouzai
    Saliha Oukid
    Aouache Mustapha
    Neural Computing and Applications, 2022, 34 : 17981 - 17998
  • [7] Machine learning modeling for the prediction of materials energy
    Mouzai, Meriem
    Oukid, Saliha
    Mustapha, Aouache
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (20): : 17981 - 17998
  • [8] Machine Learning Prediction on Properties of Nanoporous Materials Utilizing Pore Geometry Barcodes
    Zhang, Xiangyu
    Cui, Jing
    Zhang, Kexin
    Wu, Jiasheng
    Lee, Yongjin
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2019, 59 (11) : 4636 - 4644
  • [9] Machine Learning Aided Modeling of Granular Materials: A Review
    Wang, Mengqi
    Kumar, Krishna
    Feng, Y. T.
    Qu, Tongming
    Wang, Min
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2024,
  • [10] Pushing the frontiers of materials modeling and discovery with machine learning
    Sankaranarayanan, Subramanian
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 256
12345