Predicting the properties of bitumen using machine learning models trained with force field atom types and molecular dynamics simulations

被引：0
|
作者：
Assaf, Eli I. [1 ]
Liu, Xueyan [1 ]
Lin, Peng [2 ]
Ren, Shisong [1 ]
Erkens, Sandra [1 ,2 ]
机构：
[1] Delft University of Technology, Delft, Netherlands
[2] Ministry of Infrastructure and Water Management (Rijkswaterstaat), Netherlands
来源：
Materials and Design | 2024年 / 246卷
关键词：
Aromatic fraction - Asphaltene fractions - Bitumen design - Chemical descriptors - Dynamics simulation - Forcefields - Machine learning models - Machine-learning - Molecular analysis - Property;
D O I：
10.1016/j.matdes.2024.113327
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [41] Predicting Thermodynamic Properties of Alkanes by High-Throughput Force Field Simulation and Machine Learning
    Gong, Zheng
    Wu, Yanze
    Wu, Liang
    Sun, Huai
    [J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2018, 58 (12) : 2502 - 2516
  • [42] Predicting the changes in the WTI crude oil price dynamics using machine learning models
    Guliyev, Hasraddin
    Mustafayev, Eldayag
    [J]. RESOURCES POLICY, 2022, 77
  • [43] Molecular dynamics simulations of membrane properties affected by plasma ROS based on the GROMOS force field
    Hu, Yujia
    Zhao, Tong
    Zou, Liang
    Wang, Xiaolong
    Zhang, Yuantao
    [J]. BIOPHYSICAL CHEMISTRY, 2019, 253
  • [44] Generation and properties of bulk a-ZrO2 by molecular dynamics simulations with a reactive force field
    Sheikholeslam, S. Arash
    Xia, Guangrui Maggie
    Grecu, Cristian
    Ivanov, Andre
    [J]. THIN SOLID FILMS, 2015, 594 : 172 - 177
  • [45] Automating collective variable discovery from molecular dynamics simulations using machine learning
    Chittor, Achala
    Kolb, Sabrina
    Stockbridge, Randy
    [J]. BIOPHYSICAL JOURNAL, 2024, 123 (03) : 117A - 117A
  • [46] Ab Initio Molecular Cavity Quantum Electrodynamics Simulations Using Machine Learning Models
    Hu, Deping
    Huo, Pengfei
    [J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2023, 19 (08) : 2353 - 2368
  • [47] Accurate interatomic force field for molecular dynamics simulation by hybridizing classical and machine learning potentials
    Wang, Peng
    Shao, Yecheng
    Wang, Hongtao
    Yang, Wei
    [J]. EXTREME MECHANICS LETTERS, 2018, 24 : 1 - 5
  • [48] PREDICTING PHONON PROPERTIES FROM MOLECULAR DYNAMICS SIMULATIONS USING THE SPECTRAL ENERGY DENSITY
    Turney, Joseph E.
    Thomas, John A.
    McGaughey, Alan J. H.
    Amon, Cristina H.
    [J]. PROCEEDINGS OF THE ASME/JSME 8TH THERMAL ENGINEERING JOINT CONFERENCE 2011, VOL 3, 2011, : 145 - 150
  • [49] Prediction of rheological properties and ageing performance of recycled plastic modified bitumen using Machine learning models
    Salehi, Safoura
    Arashpour, Mehrdad
    Golafshani, Emadaldin Mohammadi
    Kodikara, Jayantha
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2023, 401
  • [50] Comparing oxidation of aluminum by oxygen and ozone using reactive force field molecular dynamics simulations
    Saidinik, Fateme
    Behnejad, Hassan
    [J]. JOURNAL OF NANOPARTICLE RESEARCH, 2023, 25 (05)
12345