Electrolyte Coatings for High Adhesion Interfaces in Solid-State Batteries from First Principles

Ransom, Brandi; Ramdas, Akash; Lomeli, Eder; Fidawi, Jad; Sendek, Austin; Devereaux, Tom; Reed, Evan J.; Schindler, Peter

doi:10.1021/acsami.3c04452

Cited by 5 publications

(1 citation statement)

References 56 publications

(98 reference statements)

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“…A common assumption is that two phases will not react if the distance above the convex hull is under 100 meV/atom [9], about 4 times k B T at room temperature, accounting for a kinetic stabilization barrier. This criterion is used widely in the computational materials search field vis-à-vis solidstate batteries, from finding electrode coating materials to SSEs with high ionic conductivity to materials promising for dendrite suppression [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Predicting Reactivity and Passivation of Solid-State Battery Interfaces

Lomeli,

Ransom,

Ramdas

et al. 2024

Preprint

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In this work, we build a computationally inexpensive, data-driven model that utilizes only atomistic structure information to predict the reactivity of interfaces between any candidate solid-state electrolyte material and a Li metal anode. This model is trained on data from ab initio molecular dynamics (AIMD) simulations of the time evolution of the solid electrolyte-Li metal interface for 67 different materials. Predicting the reactivity of solid state interfaces with ab initio techniques remains an elusive challenge in materials discovery and informatics, and previous work on predicting interfacial compatibility of solid-state Li-ion electrolytes and Li metal anodes has focused mainly on thermodynamic convex hull calculations. Our framework involves training machine learning models on AIMD data, thereby capturing information on both kinetics and thermodynamics, and then leveraging these models to predict the reactivity of thousands of new candidates in the span of seconds, avoiding the need for additional weeks-long AIMD simulations. We identify over 300 new chemically stable and over 780 passivating solid-electrolytes that are predicted to be thermodynamically unfavored. Our results indicate many potential solid-state electrolyte candidates have been incorrectly labeled unstable via purely thermodynamic approaches using density functional theory (DFT) energetics, and that the pool of promising, Li-stable solid-state electrolyte materials may be much larger than previously thought from screening efforts. To showcase the value of our approach, we highlight two borate materials that were identified by our model and confirmed by further AIMD calculations to likely be highly conductive and chemically stable with Li: LiB13C2 and LiB12PC.

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Section: Introductionmentioning

confidence: 99%

Predicting Reactivity and Passivation of Solid-State Battery Interfaces

Lomeli,

Ransom,

Ramdas

et al. 2024

Preprint

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Discovery of Stable Surfaces with Extreme Work Functions by High‐Throughput Density Functional Theory and Machine Learning

Schindler,

Antoniuk,

Cheon

et al. 2024

Adv Funct Materials

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The work function is the key surface property that determines the energy required to extract an electron from the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. This work presents a high‐throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that are created from 3,716 bulk materials. The number of calculated surface properties surpasses the previously largest database by a factor of ≈27. Several surfaces with an ultra‐low (<2 eV) and ultra‐high (>7 eV) work function are identified. Specifically, the (100)‐Ba‐O surface of BaMoO3 and the (001)‐F surface of Ag2F have record‐low (1.25 eV) and record‐high (9.06 eV) steady‐state work functions. Based on this database a physics‐based approach to featurize surfaces is utilized to predict the work function. The random forest model achieves a test mean absolute error (MAE) of 0.09 eV, comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function (≈ 105 faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion and electronic device applications.

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