Related papers: Atomistic modeling of bulk and grain boundary diff…
In recent years, machine learning interatomic potentials (MLIPs) have attracted significant attention as a method that enables large-scale, long-time atomistic simulations while maintaining accuracy comparable to electronic structure…
Recent experimental measurements of Ag impurity diffusion in the {\Sigma}5 (310) grain boundary (GB) in Cu revealed an unusual non-Arrhenius behavior suggestive of a possible structural transformation [Divinski et al., Phys. Rev. B 85,…
We present the results of molecular dynamics modeling on the structural properties of grain boundaries (GB) in thin polycrystalline films. The transition from crystalline boundaries with low mismatch angle to amorphous boundaries is…
Grain boundary (GB) segregation of solute atoms plays an important role in the microstructure and macroscopic mechanical properties of materials. The study of GB segregation of solute atoms using computational simulation has become one of…
X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that…
We develop a machine-learning method for coarse-graining condensed-phase molecular systems using anisotropic particles. The method extends currently available high-dimensional neural network potentials by addressing molecular anisotropy. We…
Quantum-accurate computer simulations play a central role in understanding phase-change materials (PCMs) for advanced memory technologies. However, direct quantum-mechanical simulations are necessarily limited to simplified models,…
Developing high-performance cathode materials for magnesium-ion batteries (MIBs) remains challenging because Mg$^{2+}$ ions move slowly, and conventional materials exhibit low voltage outputs. In this study, machine learning and…
Nanostructured tungsten has been reported as a possible alternative plasma-facing material due to its potential ability to self-heal radiation-induced defects, a property that is attributed to its high density of grain boundaries (GB).…
Modulation of the grain boundary properties in thermoelectric materials that have thermally activated electrical conductivity is crucial in order to achieve high performance at low temperatures. In this work, we show directly that the…
A deep learning model is employed to address the challenging problem of V2O5 nanoparticle segmentation and the correlation between the chemical composition and the geometrical features of lithiated V2O5 nanoparticles as an exemplar of a…
Lithium thiophosphates (LPS) with the composition (Li$_2$S)$_x$(P$_2$S$_5$)$_{1-x}$ are among the most promising prospective electrolyte materials for solid-state batteries (SSBs), owing to their superionic conductivity at room temperature…
Li$_{10}$Ge(PS$_6$)$_2$ (LGPS) is a highly concentrated solid electrolyte, in which Coulombic repulsion between neighboring cations is hypothesized as the underlying reason for concerted ion hopping, a mechanism common among superionic…
Grain boundary (GB) diffusion in polycrystalline materials is a physical phenomenon of great fundamental interest and practical significance. Although the accelerated ("short circuit") atomic transport along GBs has been known for decades,…
Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure…
We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the…
Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculation. In this study, we apply machine learning potentials in a…
Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions within both Si and SiO$_2$ without the use of \textit{ab…
In magnesium alloys with multiple substitutional elements, solute segregation at grain boundaries (GBs) has a strong impact on many important material characteristics, such as GB energy and mobility, and therefore, texture. Although it is…
Coarse-graining (CG) enables molecular dynamics (MD) simulations of larger systems and longer timescales that are otherwise infeasible with atomistic models. Machine learning potentials (MLPs), with their capacity to capture many-body…