Machine Learning Suggests Possible Bias in Molecular Simulations for Asphaltene Systems-Reference-Cited by-同舟云学术

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Machine Learning Suggests Possible Bias in Molecular Simulations for Asphaltene Systems

Published:2024-02-16 Issue:9 Volume:38 Page:7437-7453
ISSN:0887-0624
Container-title:Energy & Fuels
language:en
Short-container-title:Energy Fuels

Author:

Perez Felipe¹^ORCID,Ali Azeezat²,Wang Jianxin³,Patterson Joseph E.³,Kini Ramesh³,Striolo Alberto¹^ORCID

Affiliation:

1. School of Sustainable Chemical, Biological and Materials Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States

2. Department of Chemical Engineering, University College London, London WC1E 6BT, United Kingdom

3. Chevron Technical Center, Houston, Texas 77002, United States

Funder

UK EPSRC Centre for Doctoral Training in the Advanced Characterisation of Materials

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.3c04474

Reference98 articles.

1. Petroleomics and Structure–Function Relations of Crude Oils and Asphaltenes

2. Molecular-thermodynamic framework for asphaltene-oil equilibria

3. Molecular thermodynamics of asphaltene precipitation in reservoir fluids

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Computational Workflows to Predict Ensemble Property Distributions from Structures Obtained by Single-Molecule Imaging: Application to Steam-Cracked Tar, Coal-Derived Asphaltene, and Petroleum Pitch;Energy & Fuels;2024-09-03

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