Superior Low-Temperature All-Solid-State Battery Enabled by High-Ionic-Conductivity and Low-Energy-Barrier Interface

ACS Nano, Journal Year: 2024, Volume and Issue: 18(10), P. 7334 - 7345

Published: Feb. 29, 2024

All-solid-state batteries (ASSBs) working at room and mild temperature have demonstrated inspiring performances over recent years. However, the kinetic attributes of interface applicable to subzero temperatures are still unidentified, restricting low-temperature design operation. Herein, a host cathode interfaces constructed investigated unlock critical features required for cryogenic temperatures. The unstable between LiNi0.90Co0.05Mn0.05O2 (Ni90) Li6PS5Cl (LPSC) sulfide solid electrolyte (SE) results in unfavorable cathode–electrolyte interphase (CEI) sluggish lithium-ion transport across CEI. After inserting Li2ZrO3 (LZO) coating layer, activation energy Ni90@LZO/sulfide SE can be reduced from 60.19 kJ mol–1 41.39 owing suppressed interfacial reactions. Through replacing LPSC LZO layer by Li3InCl6 (LIC) halide SE, both highly stable low (25.79 mol–1) achieved, thus realizing an improved capacity retention (26.9%) −30 °C Ni90/LIC/LPSC/Li-In ASSB. Moreover, theoretical evaluation clarifies that cathode/SE with high ionic conductivity barrier favorable Li+ conduction through transfer cathode/interphase interface. These understandings may provide guidance ASSBs.

Language: Английский

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Decoding Electrochemical Processes of Lithium‐Ion Batteries by Classical Molecular Dynamics Simulations

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: 14(22)

Published: March 19, 2024

Abstract Lithium‐ion batteries (LIBs) have played an essential role in the energy storage industry and dominated power sources for consumer electronics electric vehicles. Understanding electrochemistry of LIBs at molecular scale is significant improving their performance, stability, lifetime, safety. Classical dynamics (MD) simulations could directly capture atomic motions thus provide dynamic insights into electrochemical processes ion transport during charging discharging that are usually challenging to observe experimentally, which momentous developing with superb performance. This review discusses developments MD approaches using non‐reactive force fields, reactive machine learning potential modeling chemical reactions reactants electrodes, electrolytes, electrode‐electrolyte interfaces. It also comprehensively how interactions, structures, transport, reaction affect electrode capacity, interfacial properties. Finally, remaining challenges envisioned future routes commented on high‐fidelity, effective simulation methods decode invisible interactions LIBs.

Language: Английский

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Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Language: Английский

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Discovery of high entropy garnet solid-state electrolytes via ultrafast synthesis

Energy storage materials, Journal Year: 2023, Volume and Issue: 63, P. 103053 - 103053

Published: Nov. 1, 2023

Language: Английский

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Lithium crystallization at solid interfaces

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: May 24, 2023

Understanding the electrochemical deposition of metal anodes is critical for high-energy rechargeable batteries, among which solid-state lithium batteries have attracted extensive interest. A long-standing open question how electrochemically deposited lithium-ions at interfaces with solid-electrolytes crystalize into metal. Here, using large-scale molecular dynamics simulations, we study and reveal atomistic pathways energy barriers crystallization solid interfaces. In contrast to conventional understanding, takes multi-step mediated by interfacial atoms disordered random-closed-packed configurations as intermediate steps, give rise barrier crystallization. This understanding extends applicability Ostwald's step rule atom states, enables a rational strategy lower-barrier promoting favorable states steps through engineering. Our findings rationally guided avenues engineering facilitating in electrodes can be generally applicable fast crystal growth.

Language: Английский

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Tuning collective anion motion enables superionic conductivity in solid-state halide electrolytes

Nature Chemistry, Journal Year: 2024, Volume and Issue: 16(10), P. 1584 - 1591

Published: Sept. 23, 2024

Language: Английский

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Robustness of Local Predictions in Atomistic Machine Learning Models

Journal of Chemical Theory and Computation, Journal Year: 2023, Volume and Issue: 19(22), P. 8020 - 8031

Published: Nov. 10, 2023

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from computational perspective, enabling large-scale ML-driven simulations with linear-scaling cost also allows identification posthoc interpretation contributions individual chemical environments motifs to complicated macroscopic properties. However, even though practical justifications exist local decomposition, only rigorously defined. Thus, when are used, their sensitivity training strategy or model architecture should be carefully considered. To this end, we introduce quantitative metric, which call prediction rigidity (LPR), that one assess how robust locally decomposed predictions ML are. We investigate dependence LPR aspects training, particularly composition data set, range different problems simple toy real systems. present strategies systematically enhance LPR, can used improve robustness, interpretability, transferability atomistic models.

Language: Английский

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Uncovering the Network Modifier for Highly Disordered Amorphous Li‐Garnet Glass‐Ceramics

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(16)

Published: Jan. 30, 2024

Highly disordered amorphous Li

Language: Английский

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Effect of Cation Disorder on Lithium Transport in Halide Superionic Conductors

ACS Energy Letters, Journal Year: 2024, Volume and Issue: 9(6), P. 2775 - 2781

Published: May 16, 2024

Li2ZrCl6 (LZC) is a promising solid-state electrolyte due to its affordability, moisture stability, and high ionic conductivity. We computationally investigate the role of cation disorder in LZC effect on Li-ion transport by integrating thermodynamic kinetic modeling. The results demonstrate that fast conductivity requires Li-vacancy disorder, which dependent degree Zr disorder. temperature required form equilibrium precludes any synthesis processes for achieving conductivity, rationalizing why only nonequilibrium methods, such as ball-milling, lead good Our simulations show lowers Li/vacancy order–disorder transition temperature, necessary creating Li diffusivity at room temperature. These insights raise challenge large-scale production these materials potential long-term stability their properties.

Language: Английский

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Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning

Angewandte Chemie International Edition, Journal Year: 2024, Volume and Issue: 63(22)

Published: March 22, 2024

The structure of amorphous silicon (a-Si) is widely thought as a fourfold-connected random network, and yet it defective atoms, with fewer or more than four bonds, that make particularly interesting. Despite many attempts to explain such "dangling-bond" "floating-bond" defects, respectively, unified understanding still missing. Here, we use advanced computational chemistry methods reveal the complex structural energetic landscape defects in a-Si. We study an ultra-large-scale, quantum-accurate model containing million thousands individual allowing reliable defect-related statistics be obtained. combine descriptors machine-learned atomic energies develop classification different types results suggest revision established floating-bond by showing fivefold-bonded atoms a-Si exhibit wide range local environments-analogous fivefold centers coordination chemistry. Furthermore, shown (but not threefold) tend cluster together. Our provides new insights into one most studied solids, has general implications for disordered materials beyond alone.

Language: Английский

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