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

共 50 条

[1] Predicting the diffusion coefficients of rejuvenators into bitumens using molecular dynamics, machine learning, and force field atom types
Assaf, Eli I.
Liu, Xueyan
Lin, Peng
Ren, Shisong
Erkens, Sandra
[J]. Materials and Design, 2024, 248
[2] Introducing a force-matched united atom force field to explore larger spatiotemporal domains in molecular dynamics simulations of bitumen
Assaf, Eli I.
Liu, Xueyan
Lin, Peng
Erkens, Sandra
[J]. MATERIALS & DESIGN, 2024, 240
[3] Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations
Choi, Joohee
Kang, Haisu
Lee, Ji Hee
Kwon, Sung Hyun
Lee, Seung Geol
[J]. NANOMATERIALS, 2022, 12 (14)
[4] Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations
Pattnaik, Punyaslok
Raghunathan, Shampa
Kalluri, Tarun
Bhimalapuram, Prabhakar
Jawahar, C., V
Priyakumar, U. Deva
[J]. JOURNAL OF PHYSICAL CHEMISTRY A, 2020, 124 (34): : 6954 - 6967
[5] MOLECULAR DYNAMICS SIMULATIONS OF HELIX BUNDLE PROTEINS USING UNRES FORCE FIELD AND ALL-ATOM FORCE FIELD
Gao, Kaifu
Yang, Minghui
[J]. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY, 2012, 11 (06): : 1201 - 1215
[6] Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models
Freitas, Rodolfo S. M.
Lima, Agatha P. F.
Chen, Cheng
Rochinha, Fernando A.
Mira, Daniel
Jiang, Xi
[J]. FUEL, 2022, 329
[7] Predicting Molecular Photochemistry Using Machine-Learning-Enhanced Quantum Dynamics Simulations
Richings, Gareth W.
Habershon, Scott
[J]. ACCOUNTS OF CHEMICAL RESEARCH, 2022, 55 (02) : 209 - 220
[8] Predicting the Fracture Propensity of Amorphous Silica Using Molecular Dynamics Simulations and Machine Learning
Liu, Jiahao
Yeo, Jingjie
[J]. INTERNATIONAL JOURNAL OF APPLIED MECHANICS, 2023, 15 (10)
[9] For every retinylidene a force field: Using machine learning to improve molecular simulations of rhodopsins
Erly, Andrew M.
Gerber, Benjamin I.
Li, Renee J.
Doole, Fathima T.
Nikolaev, Dmitrii M.
Ryazantsev, Mikhail N.
Struts, Andrey V.
Brown, Michael F.
[J]. BIOPHYSICAL JOURNAL, 2023, 122 (03) : 199A - 199A
[10] Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface
Shahbazi, Fatemeh
Esfahani, Mohammad Nasr
Keshmiri, Amir
Jabbari, Masoud
[J]. SCIENTIFIC REPORTS, 2024, 14 (01):

← 1 2 3 4 5 →