Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields Article Swipe

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Gabriel Krenzer , Johan Klarbring , Kasper Tolborg , Hugo Rossignol , Andrew R. McCluskey , Benjamin J. Morgan , Aron Walsh ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1021/acs.chemmater.3c01271

Superionic conductors have great potential as solid-stateelectrolytes,but the physics of type-II superionic transitions remains elusive.In this study, we employed molecular dynamics simulations, using machinelearning force fields, to investigate the type-II superionic phasetransition in & alpha;-Li3N. We characterized Li3N above and below the superionic phase transition by calculatingthe heat capacity, Li+ ion self-diffusion coefficient,and Li defect concentrations as functions of temperature. Our findingsindicate that both the Li+ self-diffusion coefficient andLi vacancy concentration follow distinct Arrhenius relationships inthe normal and superionic regimes. The activation energies for self-diffusionand Li vacancy formation decrease by a similar proportion across thesuperionic phase transition. This result suggests that the superionictransition may be driven by a decrease in defect formation energeticsrather than changes in Li transport mechanism. This insight may haveimplications for other type-II superionic materials.

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Condensed Matter Physics

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Fast ion conductor Phase transition Vacancy defect Lithium (medication) Diffusion Arrhenius equation Ion Chemical physics Materials science Electrolyte Phase (matter) Self-diffusion Condensed matter physics Activation energy Thermodynamics Chemistry Physical chemistry Physics Electrode Endocrinology Business Self-service Marketing Organic chemistry Medicine

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Type: article
Language: en
Landing Page: https://doi.org/10.1021/acs.chemmater.3c01271
OA Status: hybrid
Cited By: 13
References: 67
Related Works: 10
OpenAlex ID: https://openalex.org/W4384825749

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DOI: https://doi.org/10.1021/acs.chemmater.3c01271

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Title: Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields

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Language: en

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Publication year: 2023

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Publication date: 2023-07-19

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Authors: Gabriel Krenzer, Johan Klarbring, Kasper Tolborg, Hugo Rossignol, Andrew R. McCluskey, Benjamin J. Morgan, Aron Walsh

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Landing page: https://doi.org/10.1021/acs.chemmater.3c01271

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Concepts: Fast ion conductor, Phase transition, Vacancy defect, Lithium (medication), Diffusion, Arrhenius equation, Ion, Chemical physics, Materials science, Electrolyte, Phase (matter), Self-diffusion, Condensed matter physics, Activation energy, Thermodynamics, Chemistry, Physical chemistry, Physics, Electrode, Endocrinology, Business, Self-service, Marketing, Organic chemistry, Medicine

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Cited by: 13

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