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

---

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

---

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

---

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

---

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

---

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

---

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

---

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

---

Constant-Current Nonequilibrium Molecular Dynamics Approach for Accelerated Computation of Ionic Conductivity Including Ion-Ion Correlation

PRX Energy, Journal Year: 2025, Volume and Issue: 4(1)

Published: Feb. 19, 2025

Calculation of ionic conductivity including ion-ion correlation effects using equilibrium molecular dynamics (EMD) is computationally demanding, but the significant in many promising electrolytes such as solid electrolytes. Herein, we have developed a “constant-current” nonequilibrium MD (NEMD) simulation method, contrast to conventional constant-field approach, for correlated conductivities from constrained current with fluctuating external field. The improved efficiency constant-current NEMD approach demonstrated by applying it representative electrolyte, cubic Li7La3Zr2O12. convergence faster than that EMD 2 orders magnitude. low-temperature simulations also reveal Li7La3Zr2O12 exhibits non-Arrhenius behavior activation energy changes at around 600 K. This work presents not only high sampling calculate conductivity, importance direct computations low temperature without Arrhenius extrapolation. Published American Physical Society 2025

Language: Английский

---

Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 10, 2025

Abstract The structure of amorphous silicon has been studied for decades. two main theories are based on a continuous random network and ‘paracrystalline’ model, respectively—the latter defined as showing localized structural order resembling the crystalline state whilst retaining an overall network. However, extent this local unclear, experimental data have led to conflicting interpretations. Here we show that signatures paracrystallinity in otherwise disordered indeed compatible with observations silicon. We use quantum-mechanically accurate, machine-learning-driven simulations systematically sample configurational space quenched silicon, thereby allowing us elucidate boundary between amorphization crystallization. analyze our dataset using local-energy descriptors paracrystalline models consistent experiments both regards. Our work provides unified explanation seemingly one most widely networks.

Language: Английский

---