Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids-Reference-Cited by-同舟云学术

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Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids

Published:2023-05-16 Issue:11 Volume:19 Page:3054-3062
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Leverant Calen J.¹,Greathouse Jeffery A.²^ORCID,Harvey Jacob A.³^ORCID,Alam Todd M.⁴⁵^ORCID

Affiliation:

1. Nanoscale Sciences Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States

2. Nuclear Waste Disposal Research & Analysis Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States

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

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

5. ACC Consulting New Mexico, Cedar Crest, New Mexico 87008, United States

Funder

Sandia National Laboratories

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.2c01040

Reference61 articles.

1. Diffusion in porous crystalline materials

2. Diffusion in nanoporous materials: fundamental principles, insights and challenges

3. Perspective: Dynamics of confined liquids

4. Quantifying Pore Width Effects on Diffusivity via a Novel 3D Stochastic Approach with Input from Atomistic Molecular Dynamics Simulations

5. Integrating Adsorption and Diffusion in Nanopores Using Thermodynamics and Equations of State

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2. Can Artificial Intelligence Accelerate Fluid Mechanics Research?;Fluids;2023-07-19

3. Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods;Micromachines;2023-07-19

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