Electronic charge density as a fast approach for predicting Li-ion migration pathways in superionic conductors with first-principles level precision

Liu, Yinqiao; Jiang, Xue; Zhao, Jijun; Hu, Ming

doi:10.1016/j.commatsci.2021.110380

Cited by 11 publications

(20 citation statements)

References 21 publications

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“…[102] Descriptors can help us to efficiently search for high performance materials by locating regions of interest in phase space with limited experimental/computational cost. [22,[310][311][312] For example, the charge density can be a descriptor to ionic migration paths in solid-state electrolytes, [265] or the electronegativity of atoms are descriptors to intercalation potentials. [313] Approximate property estimation through descriptors is very fast in screening target systems.…”

Section: Deep Descriptors From MLmentioning

confidence: 99%

“…Access to electron density can help us in creating a detailed understanding of SEI formation and dynamics-from bonding and interaction type [90,261] to assigning redox reactions on atoms via changes in oxidation states. [262,263] Charge density can be used as a descriptor to map Li ion migration paths in solid-state phases [264,265] or intercalation locations. [266] While ML potentials let us break the cost-accuracy trade-off for total energy and force estimations (and derived properties from those), doing the same for volumetric data like density distribution accurately is much more of a challenge.…”

Section: Electron Density and Other Propertiesmentioning

confidence: 99%

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Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Diddens

Appiah

Mabrouk

et al. 2022

Adv Materials Inter

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The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its essential function is to prevent the electrolyte from continuous electrochemical degradation, while simultaneously allowing ions to pass through, thus constituting an electronically insulating, but ionically conducting material. Its properties crucially affect the overall performance and aging of a battery cell. Despite decades of intense research, understanding the SEI's precise formation mechanism, structure, composition, and evolution remains a conundrum. State‐of‐the‐art computational modeling techniques are powerful tools to gain additional insights, although confronted with a trade‐off between accuracy and accessible time‐ and length scales. In this review, it is discussed how recent advances in data‐driven models, especially the development of fast and accurate surrogate simulators and deep generative models, can work with physics‐based and physics‐informed approaches to enable the next generation of breakthroughs in this field. Machine learning‐enhanced multiscale models can provide new pathways to inverse the design of interphases with desired properties.

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Section: Deep Descriptors From MLmentioning

confidence: 99%

Section: Electron Density and Other Propertiesmentioning

confidence: 99%

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Diddens

Appiah

Mabrouk

et al. 2022

Adv Materials Inter

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show abstract

“…Using, for example, kinetic Monte Carlo methods working with such potentials, it is possible to estimate kinetic properties like power densities in disordered battery electrodes with sufficient accuracy to predict trends and optimize materials composition and utilization. Machine learning models can provide access to diffusion percolation networks [188] in disordered materials utilizing the predicted electron density of millions of possible disordered structures. [36] Even with fast ML potentials, it is not possible to thoroughly explore the disordered material phase space.…”

Section: Transport Processes In Ordered and Disordered Systemsmentioning

confidence: 99%

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Bhowmik¹,

Berecibar

Casas‐Cabanas

et al. 2021

Advanced Energy Materials

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BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries.Here, a description is given of how the AI-assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low-technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI-assisted toolkit developed within BIG-MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time-and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling and explainable AI, as well as AI-assisted materials characterization, self-driving labs, closed-loop optimization, and AI for advanced sensing and self-healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low-TRL battery chemistries and concepts covering multivalent anodes, metalsulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries.

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“…[32,33] Fast screening of highly diffusive materials do not require very high accuracy, so less accurate models, that still capture trends, have emerged recently, and these 4 include a novel charge density descriptor based approach. [28,[34][35][36][37] The underlying chemical understanding behind these models is that as the ions move through a host lattice containing anions of highly electronegative character, the valence electronic charge density of the framework does not depend on the position of the diffusing fully charged cations. [34] Although there exist some differences in approaches, these models simply employ local electrostatic interaction energy (with either a point charge [28,34] or finite sized ion model [36]) to create a surrogate model for the ionic diffusion as an alternative to DFT-based NEB or Ab-initio Molecular Dynamics (AIMD).…”

Section: Introductionmentioning

confidence: 99%

“…Thus one can simply optimize a pathway between two point in the electron density cloud by running a NEB algorithm using the electronic charge density as a static potential. [35,37] As such an approach requires only a single DFT self-consistent calculation (SCF) to be performed, it would be an exceptionally inexpensive way to perform optimization of diffusion paths compared to the traditional method and would give a rough ranking of the associated barrier being larger or smaller. Integration of multi-fidelity methods within workflows further reduces resource requirements for target properties that are computationally demanding like diffusion barriers.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Autonomous Workflow For Antiperovskite-based Solid State Electrolytes

Castelli

Sjølin

Jørgensen

et al. 2022

Preprint

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We developed and implemented an autonomous multi-target multi-fidelity workflow to explore the chemical space of antiperovskite materials with general formula X3AB (X = Li, Na, Mg), searching for stable high performance solid state electrolytes (SSEs) for all-solid state batteries. The workflow is based on the calculation of thermodynamic and kinetic properties, which include phase and electrochemical stability, semiconducting behavior, and ionic diffusivity. To accelerate the calculation of the kinetic properties, we implement a surrogate model able to predict the transition state structures during ionic diffusion, thus reducing the expensive calculation cost by more than one order of magnitude, keeping the error within 73 meV compared to the more accurate methods. 14 antiperovskites have been identified as possible SSE candidates. Moreover, this approach is general and chemistry neutral, so can be applied to other battery chemistries and crystal prototypes.

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Electronic charge density as a fast approach for predicting Li-ion migration pathways in superionic conductors with first-principles level precision

Cited by 11 publications

References 21 publications

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Accelerated Autonomous Workflow For Antiperovskite-based Solid State Electrolytes

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