Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12

Chen, Chi; Lu, Ziheng; Ciucci, Francesco

doi:10.1038/srep40769

Cited by 55 publications

(63 citation statements)

References 58 publications

(62 reference statements)

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“…Meier et al performed a first-principles metadynamics study of cubic LLZO and identified a concerted diffusion process in their simulation trajectory [42], and a recent first-principles study by He et al showed that concerted diffusion processes in this material can have lower potential energy barriers than single-ion hopping processes [89]. Support for single-ion hopping, however, comes from a study by Chen et al , who performed classical molecular dynamics simulations of LLZO [73]. By decomposing their simulation trajectories into sequences of single-ion hops, these authors showed that diffusion can be modelled as a Poisson process, which is a characteristic signature of an independent hopping process [64].…”

Section: Summary and Discussionmentioning

confidence: 99%

Lattice-geometry effects in garnet solid electrolytes: a lattice-gas Monte Carlo simulation study

Morgan

2017

R. Soc. open sci.

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Ionic transport in solid electrolytes can often be approximated as ions performing a sequence of hops between distinct lattice sites. If these hops are uncorrelated, quantitative relationships can be derived that connect microscopic hopping rates to macroscopic transport coefficients; i.e. tracer diffusion coefficients and ionic conductivities. In real materials, hops are uncorrelated only in the dilute limit. At non-dilute concentrations, the relationships between hopping frequency, diffusion coefficient and ionic conductivity deviate from the random walk case, with this deviation quantified by single-particle and collective correlation factors, f and fI, respectively. These factors vary between materials, and depend on the concentration of mobile particles, the nature of the interactions, and the host lattice geometry. Here, we study these correlation effects for the garnet lattice using lattice-gas Monte Carlo simulations. We find that, for non-interacting particles (volume exclusion only), single-particle correlation effects are more significant than for any previously studied three-dimensional lattice. This is attributed to the presence of two-coordinate lattice sites, which causes correlation effects intermediate between typical three-dimensional and one-dimensional lattices. Including nearest-neighbour repulsion and on-site energies produces more complex single-particle correlations and introduces collective correlations. We predict particularly strong correlation effects at xLi=3 (from site energies) and xLi=6 (from nearest-neighbour repulsion), where xLi=9 corresponds to a fully occupied lithium sublattice. Both effects are consequences of ordering of the mobile particles. Using these simulation data, we consider tuning the mobile-ion stoichiometry to maximize the ionic conductivity, and show that the ‘optimal’ composition is highly sensitive to the precise nature and strength of the microscopic interactions. Finally, we discuss the practical implications of these results in the context of lithium garnets and other solid electrolytes.

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Section: Summary and Discussionmentioning

confidence: 99%

Lattice-geometry effects in garnet solid electrolytes: a lattice-gas Monte Carlo simulation study

Morgan

2017

R. Soc. open sci.

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“…In unsupervised learning, the goal is to identify patterns from data without input labels. In materials science, it has been applied to study the collective diffusion of ions and visualize complex high‐dimensional data . Lastly, reinforcement learning mimics how humans learn by interacting with environments; the algorithm improves in its ability to perform certain tasks through feedback in the form of rewards or punishments.…”

Section: Model Selection and Trainingmentioning

confidence: 99%

“…Such algorithms however require prior knowledge of the number of clusters, i.e., the k . To solve this issue, the same authors developed a parameter‐free density‐based clustering approach for studying the fast lithium diffusion with a relatively flat potential energy surface. In a different study, Meredig and Wolverton have used cluster analysis to group defect energies in the periodic table by a x ‐means method.…”

Section: Model Selection and Trainingmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

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373

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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“…The positions of lithium during the trajectory are collapsed into the same frame and shown as small spheres, with color and reflectivity being chosen according to the site associated with the ion in that frame. sion events [30], and to design new descriptors for conductivity [28]. Ideally, an automated site analysis (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised landmark analysis for jump detection in molecular dynamics simulations

Kahle

Musaelian

Marzari

et al. 2019

Phys. Rev. Materials

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Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of the system's free energy surface. However, the analysis of trajectories for relevant but rare events, such as a jump of the diffusing mobile ion, is still rather cumbersome, requiring prior knowledge of the diffusive process in order to get meaningful results. In this work, we present a novel approach to detect the relevant events in a diffusive system without assuming prior information regarding the underlying process. We start from a projection of the atomic coordinates into a landmark basis to identify the dominant features in a mobile ion's environment. Subsequent clustering in landmark space enables a discretization of any trajectory into a sequence of distinct states. As a final step, the use of the smooth overlap of atomic positions descriptor allows distinguishing between different environments in a straightforward way. We apply this algorithm to ten Li-ionic systems and perform in-depth analyses of cubic Li7La3Zr2O12, tetragonal Li10GeP2S12, and the β-eucryptite LiAlSiO4. We compare our results to existing methods, underscoring strong points, weaknesses, and insights into the diffusive behavior of the ionic conduction in the materials investigated.

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Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12

Cited by 55 publications

References 58 publications

Lattice-geometry effects in garnet solid electrolytes: a lattice-gas Monte Carlo simulation study

Lattice-geometry effects in garnet solid electrolytes: a lattice-gas Monte Carlo simulation study

A Critical Review of Machine Learning of Energy Materials

Unsupervised landmark analysis for jump detection in molecular dynamics simulations

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