arXiv subjects

Liyi Bai

Publications and source records attributed to Liyi Bai.

5 recordsLinked to original sources

arXiv2026

Turning Insulators into Accelerators: Deciphering the Interfacial Conductivity Boost in ZrO2-Li2ZrCl6 Composites through Machine Learning Molecular Dynamics Simulations

Halide solid-state electrolytes have emerged as promising candidates for all-solid-state lithium batteries due to their high oxidative stability and deformability, yet their moderate ionic conductivity remains a bottleneck. While incorporating ionically insulating ZrO2 nanoparticles (Nat. Commun. 2023, 14, 2459) has been experimentally shown to enhance the ionic conductivity of Li2ZrCl6, the atomistic origin governing this interfacial phenomenon remains unclear. Here, we bridge the spatiotemporal gap in modeling complex heterostructures by developing an accurate machine-learned force fields based on neuroevolution potential, enabling large-scale molecular dynamics simulations of ZrO2/Li2ZrCl6 heterostructures. By systematically investigating four representative low-lattice-mismatch ZrO2/Li2ZrCl6 interfaces, we identify spontaneous interfacial amorphization driven by space-charge effects upon surface cleavage, trapping Li+ and leading to under-coordinated Li+ polyhedrons with pronounced geometric distortion. These distorted amorphous interfacial regions exhibit markedly enhanced Li+ hopping activity, significantly outperforming the bulk lattice, provided that local mobile Li+ inventory is not depleted by surface charge redistribution. This work establishes a computational framework for training validated machine-learned force fields for interfaces and provides mechanistic understandings of the interfacial conductivity boost in the insulator-conductor composites, guiding the rational design of electrolytes toward next-generation solid-state batteries.

cond-mat.mtrl-sci↗

arXiv2025

Observing Nucleation and Crystallization of Rocksalt LiF from Molten State through Molecular Dynamics Simulations with Refined Machine-Learned Force Field

Lithium fluoride (LiF) is a critical component for stabilizing lithium metal anode and high-voltage cathodes towards the next-generation high-energy-density lithium batteries. Recent modeling study reported the formation of wurtzite LiF below about 550 K (J. Am. Chem. Soc. 2023, 145, 1327-1333), in contrast to experimental observation of rocksalt LiF under ambient conditions. To address this discrepancy, we employ molecular dynamics (MD) simulations with a refined machine-learned force field (MLFF), and demonstrate the nucleation and crystallization of rocksalt LiF from the molten phase at temperatures below about 800 K. The rocksalt phase remains stable in LiF nanoparticles. Complementary density functional theory (DFT) calculations show that dispersion interactions are essential for correctly predicting the thermodynamic stability of rocksalt LiF over the wurtzite phase on top of the commonly used PBE functional. Furthermore, we show that inclusion of virial stresses--alongside energies and forces--in the training of MLFFs is crucial for capturing phase nucleation and crystallization of rocksalt LiF under the isothermal-isobaric ensemble. These findings underscore the critical role of dispersion interactions in atomistic simulations of battery materials, where such effects are often non-negligible, and highlight the necessity of incorporating virial stresses during the training of MLFF to enable accurate modeling of solid-state systems.

cond-mat.mtrl-sci↗

arXiv2025

Structural and transport properties of LiTFSI/G3 electrolyte with machine-learned molecular dynamics

The lithium bis(trifluoromethylsulfonyl)azanide-triglyme electrolyte plays a critical role in the performance of lithium-ion batteries. However, its solvation structure and transport properties at the atomic scale remain incompletely understood. In this study, we develop an efficient and accurate neuroevolution potential (NEP) model by integrating bootstrap and active learning strategies. Using machine-learned NEP-driven molecular dynamics simulations, we explore the structural and diffusion properties of LiTFSI/G3 across a wide range of the solute-to-solvent ratios, systematically analyzing electrolyte density, ion coordination, viscosity, and lithium self-diffusion. The computed densities show excellent agreement with experimental data, and pair correlation analysis reveals significant interactions between lithium ions and surrounding oxygen atoms, which strongly impacts Li$^+$ mobility. Viscosity and diffusion calculations further demonstrate that increasing LiTFSI concentration enhances Li-O interactions, resulting in higher viscosity and reduced lithium diffusion. Additionally, machine learning-based path integral molecular dynamics (PIMD) simulations confirm the negligible impact of quantum effects on Li$^+$ transport. The electrolyte-specific protocol developed in this work provides a systematic framework for constructing high-fidelity machine learning potentials for complex systems.

cond-mat.mtrl-sci↗

arXiv2024

Intrinsic ferroelectric switching in two-dimension $α$-In$_2$Se$_3$

Two-dimensional (2D) ferroelectric semiconductors present opportunities for integrating ferroelectrics into high-density ultrathin nanoelectronics. Among the few synthesized 2D ferroelectrics, $α$-In$_2$Se$_3$, known for its electrically addressable vertical polarization has attracted significant interest. However, the understanding of many fundamental characteristics of this material, such as the existence of spontaneous in-plane polarization and switching mechanisms, remains controversial, marked by conflicting experimental and theoretical results. Here, our combined experimental characterizations with piezoresponse force microscope and symmetry analysis conclusively dismiss previous claims of in-plane ferroelectricity in $α$-In$_2$Se$_3$. The processes of vertical polarization switching in monolayer $α$-In$_2$Se$_3$ are explored with deep-learning-assisted large-scale molecular dynamics simulations, revealing atomistic mechanisms fundamentally different from those of bulk ferroelectrics. Despite lacking in-plane effective polarization, 1D domain walls can be moved by both out-of-plane and in-plane fields, exhibiting unusual avalanche dynamics characterized by abrupt, intermittent moving patterns. The propagating velocity at various temperatures, field orientations, and strengths can be statistically described with a universal creep equation, featuring a dynamical exponent of 2 that is distinct from all known values for elastic interfaces moving in disordered media. This work rectifies a long-held misunderstanding regarding the in-plane ferroelectricity of $α$-In$_2$Se$_3$, and the quantitative characterizations of domain wall velocity will hold broad implications for both the fundamental understanding and technological applications of 2D ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2021

Accurate force field of two-dimensional ferroelectrics from deep learning

The discovery of two-dimensional (2D) ferroelectrics with switchable out-of-plane polarization such as monolayer $α$-In$_2$Se$_3$ offers a new avenue for ultrathin high-density ferroelectric-based nanoelectronics such as ferroelectric field effect transistors and memristors. The functionality of ferroelectrics depends critically on the dynamics of polarization switching in response to an external electric/stress field. Unlike the switching dynamics in bulk ferroelectrics that have been extensively studied, the mechanisms and dynamics of polarization switching in 2D remain largely unexplored. Molecular dynamics (MD) using classical force fields is a reliable and efficient method for large-scale simulations of dynamical processes with atomic resolution. Here we developed a deep neural network-based force field of monolayer In$_2$Se$_3$ using a concurrent learning procedure that efficiently updates the first-principles-based training database. The model potential has accuracy comparable with density functional theory (DFT), capable of predicting a range of thermodynamic properties of In$_2$Se$_3$ polymorphs and lattice dynamics of ferroelectric In$_2$Se$_3$. Pertinent to the switching dynamics, the model potential also reproduces the DFT kinetic pathways of polarization reversal and 180$^\circ$ domain wall motions. Moreover, isobaric-isothermal ensemble MD simulations predict a temperature-driven $α\rightarrow β$ phase transition at the single-layer limit, as revealed by both local atomic displacement and Steinhardt's bond orientational order parameter $Q_4$. Our work paves the way for further research on the dynamics of ferroelectric $α$-In$_2$Se$_3$ and related systems.

cond-mat.mtrl-sci↗