Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids-Reference-Cited by-同舟云学术

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Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids

Published:2020-11-25 Issue:24 Volume:11 Page:10375-10381
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Leverant Calen J.¹²^ORCID,Harvey Jacob A.³^ORCID,Alam Todd M.⁴^ORCID

Affiliation:

1. Department of WMD Threats & Aerosol Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States

2. Department of Chemical Engineering, University of Florida, Gainesville, Florida 32611, United States

3. Department of Geochemistry, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States

4. Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States

Funder

Laboratory Directed Research and Development

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.0c03108

Reference39 articles.

1. Importance of mass transfer and intraparticle diffusion in polymer-supported phase-transfer catalysis

3. Bulk separation of multicomponent gas mixtures by pressure swing adsorption: Pore/surface diffusion and equilibrium models

4. Maxwell–Stefan Diffusivities in Binary Mixtures of Ionic Liquids with Dimethyl Sulfoxide (DMSO) and H2O

5. Prediction of Composition-Dependent Self-Diffusion Coefficients in Binary Liquid Mixtures: The Missing Link for Darken-Based Models

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