Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes using Artificial Neural Networks-Reference-Cited by-同舟云学术

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Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes using Artificial Neural Networks

Published:2020-07-16 Issue:9 Volume:3 Page:2000097
ISSN:2513-0390
Container-title:Advanced Theory and Simulations
language:en
Short-container-title:Adv. Theory Simul.

Author:

Rao Karun K.¹²^ORCID,Yao Yan³²^ORCID,Grabow Lars C.¹²^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering University of Houston Houston TX 77004 USA

2. Texas Center for Superconductivity at the University of Houston University of Houston Houston TX 77004 USA

3. Department of Electrical and Computer Engineering University of Houston Houston TX 77004 USA

Funder

U.S. Department of Energy

National Science Foundation

National Aeronautics and Space Administration

Publisher

Wiley

Subject

Multidisciplinary,Modeling and Simulation,Numerical Analysis,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202000097

Reference58 articles.

1. A review of lithium and non-lithium based solid state batteries

2. A sulphide lithium super ion conductor is superior to liquid ion conductors for use in rechargeable batteries

3. Liquid‐Like Ionic Conduction in Solid Lithium and Sodium Monocarba‐ closo ‐Decaborates Near or at Room Temperature

4. Recent advances in all-solid-state rechargeable lithium batteries

5. All-Solid-State Lithium-Ion Microbatteries: A Review of Various Three-Dimensional Concepts

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2. Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review;Journal of the American Ceramic Society;2024-06-09

3. Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte;Advanced Energy Materials;2024-02-14

4. Nanosecond MD of battery cathode materials with electron density description;Energy Storage Materials;2023-11

5. Battery state-of-charge measurement and control model based on the Internet platform;State Estimation Strategies in Lithium-ion Battery Management Systems;2023

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