Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Simon Anniés, Chiara Panosetti, Maria Voronenko, Dario Mauth, Christiane Rahe, Christoph Scheurer
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	lithium-ion batteries DFTB Li-GIC graphite intercalation multiscale modeling Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/58759729fbe748958fb00d51f59983d8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:58759729fbe748958fb00d51f59983d8
record_format	dspace
spelling	oai:doaj.org-article:58759729fbe748958fb00d51f59983d82021-11-11T18:09:34ZAccessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential10.3390/ma142166331996-1944https://doaj.org/article/58759729fbe748958fb00d51f59983d82021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6633https://doaj.org/toc/1996-1944Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC<sub>6−<i>x</i></sub>; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>x</mi><mo>></mo><mn>0</mn></mrow></semantics></math></inline-formula>) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations.Simon AnniésChiara PanosettiMaria VoronenkoDario MauthChristiane RaheChristoph ScheurerMDPI AGarticlelithium-ion batteriesDFTBLi-GICgraphiteintercalationmultiscale modelingTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6633, p 6633 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	lithium-ion batteries DFTB Li-GIC graphite intercalation multiscale modeling Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	lithium-ion batteries DFTB Li-GIC graphite intercalation multiscale modeling Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Simon Anniés Chiara Panosetti Maria Voronenko Dario Mauth Christiane Rahe Christoph Scheurer Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential
description	Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC<sub>6−<i>x</i></sub>; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>x</mi><mo>></mo><mn>0</mn></mrow></semantics></math></inline-formula>) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations.
format	article
author	Simon Anniés Chiara Panosetti Maria Voronenko Dario Mauth Christiane Rahe Christoph Scheurer
author_facet	Simon Anniés Chiara Panosetti Maria Voronenko Dario Mauth Christiane Rahe Christoph Scheurer
author_sort	Simon Anniés
title	Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential
title_short	Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential
title_full	Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential
title_fullStr	Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential
title_full_unstemmed	Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential
title_sort	accessing structural, electronic, transport and mesoscale properties of li-gics via a complete dftb model with machine-learned repulsion potential
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/58759729fbe748958fb00d51f59983d8
work_keys_str_mv	AT simonannies accessingstructuralelectronictransportandmesoscalepropertiesofligicsviaacompletedftbmodelwithmachinelearnedrepulsionpotential AT chiarapanosetti accessingstructuralelectronictransportandmesoscalepropertiesofligicsviaacompletedftbmodelwithmachinelearnedrepulsionpotential AT mariavoronenko accessingstructuralelectronictransportandmesoscalepropertiesofligicsviaacompletedftbmodelwithmachinelearnedrepulsionpotential AT dariomauth accessingstructuralelectronictransportandmesoscalepropertiesofligicsviaacompletedftbmodelwithmachinelearnedrepulsionpotential AT christianerahe accessingstructuralelectronictransportandmesoscalepropertiesofligicsviaacompletedftbmodelwithmachinelearnedrepulsionpotential AT christophscheurer accessingstructuralelectronictransportandmesoscalepropertiesofligicsviaacompletedftbmodelwithmachinelearnedrepulsionpotential
_version_	1718431941979537408

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

Ejemplares similares