arXiv subjects

Yongliang Ou

Publications and source records attributed to Yongliang Ou.

7 recordsLinked to original sources

arXiv2026

An Ontology for Machine Learning Interatomic Potentials

Machine learning interatomic potentials (MLIPs) approximate quantum-mechanical energies and forces---conventionally computed by density functional theory (DFT) or wave-function methods---at a fraction of the cost. The field encompasses a growing ecosystem of algorithms, training datasets, hyperparameters, and target materials, yet the metadata needed to systematically compare, reproduce, and build upon MLIP studies remains scattered across papers, scripts, and ad-hoc file formats. We present the MLIPs ontology, an OWL 2 DL ontology that captures the concepts needed to describe MLIP methods, their hyperparameters, training datasets with DFT provenance, and published benchmarks. The ontology is organized into three modules---Method, Training Data, and Benchmark---and connects existing ontologies in materials science (MDO, CMSO/ASMO) and machine learning (ML-Schema), complementing dataset-side schemas such as Croissant. It declares 27 formal axioms enforcing data completeness and consistency, including property chains that link trained models to their methods and training data. We demonstrate the ontology through a running example based on Moment Tensor Potentials and evaluate it through competency-question execution on a 20-paper seeded knowledge graph, OWL reasoning, and comparison with existing ontologies.

cs.AI↗

arXiv2026

Microstructural Insights into Fast Ion Transport in Solid Electrolytes via Multiscale Modeling

Improving solid electrolytes is critical for high-performance all-solid-state batteries, yet the microstructural features that enable fast ion transport remain poorly understood. Here, we use multiscale modeling to resolve polycrystalline ion transport from atomic-scale hopping at grain boundaries to continuum-scale percolation, thereby providing insights into realistic solid-electrolyte microstructures. Accurate lightweight machine-learning potentials -- developed via closed-loop active learning for exemplar argyrodites Li$_6$PS$_5$X, X $\in$ {Cl, Br, I} -- are employed to integrate molecular dynamics with finite element simulations. We find that diffusion barriers of the anion-ordered bulk scale linearly with anion radius. Grain boundaries exert opposite effects depending on the bulk: enhancing ion diffusion in low-diffusivity phases but suppressing it in fast-diffusing ones. Li$_6$PS$_5$I exhibits non-Arrhenius transport behavior consistent with experimental observations. Our results clarify the pivotal role of grain boundaries in ion transport and guide a priori microstructural design of advanced solid electrolytes.

cond-mat.mtrl-sci↗

arXiv2026

An experimentally validated end-to-end framework for operando modeling of intrinsically complex metallosilicates

Structurally and chemically complex materials such as amorphous metallosilicates underpin major catalytic and separation technologies, yet their intrinsic complexity challenges reliable atomistic modeling under realistic conditions. Consequently, simulations that connect composition to material properties remain largely inaccessible for these materials. Here, we enable quantitative operando atomistic modeling of intrinsically complex materials through an experimentally validated end-to-end computational framework. The approach combines separation of simulation domains, lightweight machine-learning potentials trained on high-fidelity data, and large-scale de novo in silico synthesis that mimics experimental procedures. We apply the framework to realistic mesoporous SiO$_2$(Al$_2$O$_3$)$_{x/2}$ (0 $\leq x \leq$ 0.4) and validate the results experimentally. Simulations quantitatively reproduce multiple experimental observables, including bulk densities, pair distribution functions, infrared spectra, and hydroxyl densities. Beyond prediction, the framework enables analysis of acid sites and vibrations for catalytic and adsorption processes. By integrating simulation and experiment within a unified workflow, we advance the realism and reliability of atomistic modeling for intrinsically complex materials.

cond-mat.mtrl-sci↗

arXiv2026

Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions

Machine-learning interatomic potentials (MLIPs) enable large-scale atomistic simulations at moderate computational cost while retaining ab initio accuracy. MLIPs trained on coupled-cluster data, particularly CCSD(T), have emerged as a promising route to achieve chemical accuracy beyond the limits of density functional theory (DFT) and to incorporate non-empirical van der Waals (vdW) interactions. Most existing approaches are, however, still not straightforwardly applicable for systems with extended covalent networks such as covalent organic frameworks (COFs) due to the limited availability of CCSD(T) for periodic systems. Here we present a methodology to train MLIPs with CCSD(T) accuracy for these systems. The approach uses the Δ-learning method with a dispersion-corrected tight-binding baseline. This strategy enables training on compact molecular fragments while preserving transferability toward the periodic systems. Dispersion interactions are accounted for by adding vdW-bound multimers in the training set, and the combination with a vdW-aware tight-binding baseline allows the formally local MLIP to attain CCSD(T)-level accuracy even for systems dominated by long-range vdW forces. The resulting potential yields root-mean-square energy errors below 0.4 meV/atom on training and test sets and reproduces electronic total atomization energies, bond lengths, harmonic vibrational frequencies, and inter-molecular interaction energies for benchmark molecular systems. We apply the method to a prototypical quasi-two-dimensional COF composed of carbon and hydrogen. The COF structure, inter-layer binding energies, and hydrogen absorption are analyzed at CCSD(T) accuracy. The developed methodology opens a practical route to large-scale atomistic simulations for systems with extended covalent networks and vdW interactions with chemical accuracy.

cond-mat.mtrl-sci↗

arXiv2025

A collapsed interface approach to resolve grain boundaries in finite element simulations of polycrystalline diffusion

Atomic diffusion affects the properties of various engineering materials, which predominantly occur in the polycrystalline state. A rigorous description of polycrystalline diffusion must therefore account for crystallographic defects, especially grain boundaries (GBs), whose structure and volume fraction - and hence the effective grain size - govern mass transport. Experiments and atomistic simulations consistently show that GBs can accelerate diffusion by up to several orders of magnitude and that fluxes along and across the interface are generally anisotropic. Conventional mesoscale models either neglect GBs or invoke idealized analytical corrections. Fully resolved finite-element meshes are accurate but computationally infeasible when nanometer-thin GB layers are involved. We introduce a collapsed-interface finite element that integrates the GB thickness analytically and embeds the result in a two-dimensional surface element. The formulation (i) treats in-plane and through-plane diffusivity independently, (ii) couples to the surrounding grain matrix without the need for mesh manipulations, and (iii) parametrizes both grain size and GB volume fraction via simple affine scalings, allowing systematic variation without remeshing. Effective diffusivity tensors are extracted by linear computational homogenization. The new finite element reproduces three-dimensional GB transport phenomena - channeled fluxes, concentration discontinuities - at a fraction of the computational cost of explicit models. Parametric studies spanning multiple orders of magnitude in GB diffusivity reveal four distinct diffusion regimes and quantify their impact on the overall response. The framework thus connects atomistic data and continuum predictions, providing an efficient tool for diffusion-driven design and optimization of polycrystalline materials.

cond-mat.mtrl-sci↗

arXiv2024

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li$_6$PS$_5$Cl using machine-learning interatomic potentials

Li$_6$PS$_5$Cl is a promising candidate for the solid electrolyte in all-solid-state Li-ion batteries. In applications, this material is in a polycrystalline state with grain boundaries (GBs) that can affect ionic conductivity. While atomistic modeling provides valuable information on the impact of GBs on Li diffusion, such studies face either high computational cost (\textit{ab initio} methods) or accuracy limitations (classical potentials) as challenges. Here, we develop a quality-level-based active learning scheme for efficient and systematic development of \textit{ab initio}-based machine-learning interatomic potentials, specifically moment tensor potentials (MTPs), for large-scale, long-time, and high-accuracy simulations of complex atomic structures and diffusion mechanisms as encountered in solid electrolytes. Based on this scheme, we obtain MTPs for Li$_6$PS$_5$Cl and investigate two tilt GBs, $\Sigma3(1\bar{1}2)[110]$, $\Sigma3(\bar{1}11)[110]$, and one twist GB, $\Sigma5(001)[001]$. All three GBs exhibit low formation energies of less than \SI{20}{meV/\angstrom\textsuperscript{2}}, indicating their high stability in polycrystalline Li$_6$PS$_5$Cl. Using the MTPs, diffusion coefficients of the anion-ordered and anion-disordered bulk, as well as the three GBs, are obtained from molecular dynamics simulations of atomistic models. At \SI{300}{\kelvin}, the GB diffusion coefficients fall between the ones of the anion-ordered bulk structure (\SI{0.012e-7}{cm^2/s}, corresponding ionic conductivity about \SI{0.2}{mS/cm}) and the anion-disordered bulk structure (\SI{50}{\percent} Cl/S-anion disorder; \SI{2.203e-7}{cm^2/s}, about \SI{29.8}{mS/cm}) of Li$_6$PS$_5$Cl. Experimental data fall between the Arrhenius-extrapolated diffusion coefficients of the investigated atomic structures.

cond-mat.mtrl-sci↗

arXiv2022

Dynamic stabilization of perovskites at elevated temperatures: A comparison between cubic BaFeO$_{\textbf{3}}$ and vacancy-ordered monoclinic BaFeO$_{\textbf{2.67}}$

The impact of ordered vacancies on the dynamic stability of perovskites is investigated under the $\textit{ab initio}$ framework with a focus on cubic BaFeO$_{3}$ ($Pm\bar{3}m$) and vacancy-ordered monoclinic BaFeO$_{2.67}$ ($P2_{1}/m$). The harmonic approximation shows that both structures are dynamically unstable at 0 K. For the monoclinic structure, the instability is related to rotational distortions of the Fe coordination tetrahedra near the ordered vacancies. $\textit{Ab initio}$ molecular dynamics simulations in combination with the introduced structural descriptor demonstrate that both structures are stabilized above 130 K. Our results suggest that the ordered vacancies do not significantly alter the critical temperature at which Ba$-$Fe$-$O perovskites are dynamically stabilized. Further, strong anharmonicity for the vacancy-ordered structure above its critical temperature is revealed by a significant asymmetry of the trajectories of O anions near the ordered vacancies.

cond-mat.mtrl-sci↗