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Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
So Fujikake1, Volker L Deringer1, Tae Hoon Lee2
1Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.
The Journal of Chemical Physics
|July 2, 2018
Summary
Machine learning potentials model lithium atom interactions in carbon hosts. This approach improves atomistic simulations for battery materials by modeling guest-host interactions efficiently.
Area of Science:
- Materials Science
- Computational Chemistry
- Machine Learning
Background:
- Accurate modeling of guest atoms within host structures is crucial for understanding materials properties.
- Machine learning interatomic potentials offer a promising avenue for efficient atomistic simulations.
Purpose of the Study:
- To demonstrate the application of machine learning potentials for modeling guest atom intercalation in carbon-based host structures.
- To develop and validate Gaussian approximation potential (GAP) models for lithium-carbon interactions.
Main Methods:
- Generated Gaussian approximation potential (GAP) models for lithium-carbon interactions using density functional theory data.
- Modeled energy and force differences from lithium intercalation separately and added them to existing carbon GAP models.
- Incorporated an explicit pair potential to capture effective lithium-lithium interactions.
Main Results:
- Successfully modeled the intercalation of lithium atoms into graphene, graphite, and disordered carbon nanostructures.
- Demonstrated that modeling guest-host interactions separately improves the accuracy of the machine learning potential.
- Showcased the benefit of including pair potentials for effective lithium-lithium interactions.
Conclusions:
- This study provides a proof-of-concept for using machine learning potentials to model guest atoms in host frameworks.
- The developed approach is promising for detailed atomistic studies of battery materials, particularly for lithium-ion batteries.
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