Study of diffusion and conduction in lithium garnet oxides LixLa3Zrx−5Ta7−xO12 by machine learning interatomic potentials

Dai, Jin; Jiang, Yue; Lai, Wei

doi:10.1039/d2cp00591c

Cited by 8 publications

(2 citation statements)

References 51 publications

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“…Hotcent and TANGO codes provide default values for any other parameter not explicitly described here. This methodology has also been used by other authors to parametrize the Al–O interaction in ultrathin films; , applied to Li–P, Li–O, and Li–N to study the structural and dynamical properties of crystalline and amorphous lithium phosphorus oxynitride; and used to study diffusion and conduction in lithium garnet oxides . It has also been used to perform nonadiabatic molecular dynamics of PAH-related complexes …”

Section: Methodsmentioning

confidence: 99%

Density Functional Tight-Binding Model for Lithium–Silicon Alloys

et al. 2023

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The predictive power of molecular dynamic simulations is mainly restricted by the time scale and model accuracy. Many systems of current relevance are of such complexity that they require addressing both issues simultaneously. This is the case of silicon electrodes in Li-ion batteries, where different Li x Si alloys are formed during charge/discharge cycles. While first-principles treatments for this system are seriously limited by the computational cost of exploring its large conformational space, classical force fields are not transferable enough to represent it accurately. Density Functional Tight Binding (DFTB) is an intermediate complexity approach capable of capturing the electronic nature of different environments with a relatively low computational cost. In this work, we present a new set of DFTB parameters suited to model amorphous Li x Si alloys. Li x Si is the usual finding upon cycling the Si electrodes in the presence of Li ions. The model parameters are constructed with a particular emphasis on their transferability for the entire Li x Si composition range. This is achieved by introducing a new optimization procedure that weights stoichiometries differently to improve the prediction of their formation energies. The resulting model is shown to be robust for predicting crystal and amorphous structures for the different compositions, giving excellent agreement with DFT calculations and outperforming state-of-the-art ReaxFF potentials.

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Section: Methodsmentioning

confidence: 99%

Density Functional Tight-Binding Model for Lithium–Silicon Alloys

et al. 2023

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“…22–25 Combined with the latest development of high-dimensional description of atomic systems, ML has gradually become one of the effective methods for developing interatomic potential functions. 26–29 Especially, remarkable achievements have been made in learning potential functions of small molecules by ML methods. 17,27,30 Alternative methods are available for constructing potential functions using ML, the main algorithms used include artificial neural networks (ANNs), Gaussian progress regression (GPR) and the genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%