The resistive nature of decomposing interfaces of solid electrolytes with alkali metal electrodes

Wang, Juefan; Panchal, Abhishek A.; Gautam, Gopalakrishnan Sai; Canepa, Pieremanuele

doi:10.1039/d2ta02202h

Cited by 23 publications

(41 citation statements)

References 71 publications

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“…Another important parameter required in the fitting of MTPs is the radius cutoff (R cut ) used to ensure the smooth behavior of MTPs when atoms leave or enter the interaction neighborhood [33]. Here, we chose R cut to be 5 Å, which has been used in previous reports [32,33,[59][60][61]. While fitting the MTPs, a reasonable choice of weights associated to the energy and force terms is also needed.…”

Section: Strategies For Constructing Moment Tensor Potentialsmentioning

confidence: 99%

“…While fitting the MTPs, a reasonable choice of weights associated to the energy and force terms is also needed. In this work, we used a ratio of weights of 10:1 for energies to forces, similar to our previous work [61]. The original training set containing AIMD snapshots is randomly split into two parts-a 'training set' and a 'testing set' with a ratio of 90:10.…”

Section: Strategies For Constructing Moment Tensor Potentialsmentioning

confidence: 99%

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Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes

2023

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Ion transport in materials is routinely probed through several experimental techniques, which introduce variability in reported ionic diffusivities and conductivities. The computational prediction of ionic diffusivities and conductivities helps in identifying good ionic conductors, and suitable solid electrolytes, thus establishing firm structure-property relationships. Machine-learned potentials are an attractive strategy extending the capabilities of accurate \emph{ab initio} molecular dynamics to longer and larger simulations, enabling simulations of ion transport at low-temperature. However, being machine-learned potentials in their infancy, critical assessments of their predicting capabilities are seldom. Here, we identified the main factors controlling the quality of machine-learning potentials based on the moment tensor potential when applied to the properties of ion transport in solid electrolytes. Our results underline the importance of high-quality training sets in fitting moment tensor potentials, diverse training sets, and the importance of intrinsic defects which may occur in solid electrolytes. We demonstrate the limitations posed by short-time scale and high-temperature AIMD simulations to predict the room-temperature properties of materials.

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Section: Strategies For Constructing Moment Tensor Potentialsmentioning

confidence: 99%

Section: Strategies For Constructing Moment Tensor Potentialsmentioning

confidence: 99%

Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes

2023

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“…Albeit more accurate than classical IPs, current MLIPs are not physically interpretable and may suffer from overfitting. The transferability of MLIPs between similar chemical systems, e.g., polymorphs of the same material, appears contingent on the training set Kinetic Monte Carlo (kMC) enables the evaluation of large stochastic ensemble of ion migration events. , In kMC, the frequency Γ (eq ) of ion migration event is evaluated through the transition state theory. , normalΓ = ν * nobreak0em0.25em⁢ exp ( − E m k B T ) where ν* (measured in Hz) is the attempt frequency, associated with the vibrational modes of atoms.…”

Section: Computation To Predict Ion Transport In Materialsmentioning

confidence: 99%

Section: Computation To Predict Ion Transport In Materialsmentioning

confidence: 99%

“…Presently, both MLIP and kMC-type models remain confined to bulk systems . In this context, Dawson and collaborators have applied successfully classical interatomic potentials through molecular dynamics to study the effects of grain boundaries (homogeneous interfaces) on ion transport. , Lately, Wang et al have attempted the application of MLIP to the study of Li-ion transport in heterogeneous interfaces typically formed in batteries . The description of transport phenomena at homogeneous and heterogeneous interfaces, such as the electrode and electrolyte interfaces, remains an active area of research.…”

Section: Outlook and Final Remarksmentioning

confidence: 99%

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Pushing Forward Simulation Techniques of Ion Transport in Ion Conductors for Energy Materials

Canepa

2022

ACS Mater. Au

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Simulation techniques are crucial to establish a firm link between phenomena occurring at the atomic scale and macroscopic observations of functional materials. Importantly, extensive sampling of space and time scales is paramount to ensure good convergence of physically relevant quantities to describe ion transport in energy materials. Here, a number of simulation methods to address ion transport in energy materials are discussed, with the pros and cons of each methodology put forward. Emphasis is given to the stochastic nature of results produced by kinetic Monte Carlo, which can adequately account for compositional disorder across multiple sublattices in solids.

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In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Aldossary,

Campos‐Gonzalez‐Angulo,

Pablo‐García

et al. 2024

Advanced Materials

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Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

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The resistive nature of decomposing interfaces of solid electrolytes with alkali metal electrodes

Abstract: A crucial ingredient in lithium (Li) and sodium (Na)-ion batteries (LIBs and NIBs) is the electrolytes. The use of Li-metal (Na-metal) as anode in liquid electrolyte LIBs (NIBs) is constrained...

Cited by 23 publications

References 71 publications

Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes

Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes

Pushing Forward Simulation Techniques of Ion Transport in Ion Conductors for Energy Materials

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

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