arXiv subjects

Penghua Ying

Publications and source records attributed to Penghua Ying.

At least 19 recordsLinked to original sources

arXiv2026

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements

While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use. Here, we introduce NEP89, a foundation model based on neuroevolution potential architecture, delivering near-empirical-potential speed and high accuracy across 89 elements. A compact yet comprehensive training dataset covering inorganic and organic materials was curated through descriptor-space subsampling and iterative refinement across multiple datasets. NEP89 achieves competitive accuracy compared to representative foundation models while being three to four orders of magnitude more computationally efficient, enabling previously impractical large-scale atomistic simulations of inorganic and organic systems. In addition to its out-of-the-box applicability to diverse scenarios, including million-atom-scale compression of compositionally complex alloys, ion diffusion in solid-state electrolytes and water, rocksalt dissolution, methane combustion, and protein-ligand dynamics, NEP89 also supports fine-tuning for rapid adaptation to user-specific applications, such as mechanical, thermal, structural, and spectral properties of two-dimensional materials, metallic glasses, and organic crystals.

cond-mat.mtrl-sci↗

arXiv2026

Side-Chain Tuning of Thermal-Expansion Crossover in Metal-Organic Frameworks

Achieving continuous control over macroscopic thermal expansion remains a fundamental challenge in solid-state physics. Using classical and path-integral molecular dynamics alongside lattice dynamics at near-\emph{ab initio} accuracy, we report an entropy-driven thermal-expansion crossover from positive (PTE) to negative thermal expansion (NTE) in alkoxy-functionalized MOF-5, an archetypal metal-organic framework (MOF). We demonstrate that this non-linear response is continuously tunable via the alkoxy side-chain length, quantified by the number of carbon atoms $n$ grafted onto the archetypal cubic MOF-5 framework: systems with short chains ($n \le 2$) exhibit monotonic NTE, whereas longer chains ($n \ge 3$) trigger a pronounced PTE-to-NTE crossover. At low temperatures, thermal activation of longer side chains opens additional conformational states and generates steric pressure inside the pore, driving positive expansion through a gain in side-chain conformational entropy. Conversely, at elevated temperatures, the side chains enhance transverse linker fluctuations and strengthen the string-tension mechanism associated with low-frequency framework modes, causing structural contraction favored by framework vibrational entropy. Finally, by varying the concentration of side-chain-functionalized linkers, the thermal expansion coefficient can be continuously regulated to realize negative, near-zero, and positive thermal expansion within selected temperature windows. These results establish side-chain engineering as a practical route for programming macroscopic thermodynamic responses in MOFs.

cond-mat.mtrl-sci↗

arXiv2026

Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys

Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are essential for atomistic simulations of compositionally complex alloys, but their deployment in large-scale molecular dynamics requires a balance among accuracy, efficiency, stability, transferability, and uncertainty quantification. Here, we benchmark two chemically scalable MLIP frameworks, neuroevolution potential (NEP) and graph atomic cluster expansion (GRACE), for 16 elemental metals and their multicomponent alloys. GRACE-FS shows higher training efficiency and generally better average accuracy, chemical transferability, and finite-temperature robustness, whereas UNEP-v1 provides substantially higher inference speed and remains competitive in selected stress and large-error metrics. We further show that chemical transferability is closely linked to high-temperature MD stability in highly multicomponent environments and that ensemble-based uncertainty provides a more reliable error indicator than D-optimality for the heterogeneous systems considered here. Finally, three-million-atom shock simulations demonstrate that UNEP-v1, combined with ensemble uncertainty, enables uncertainty-aware simulations under extreme dynamic conditions, yielding robust global spall-strength predictions while revealing model sensitivity in local damage pathways. These results provide practical guidelines for selecting and deploying MLIPs in large-scale simulations of multicomponent alloys.

cond-mat.mtrl-sci↗

arXiv2026

Structurally Triggered Breakdown of the Phonon Gas Model in Crystalline Metal-Organic Frameworks

While crystalline materials with glass-like thermal conductivity are fundamentally intriguing, structurally triggering the transition from propagating to diffusive heat transport within a single framework remains a formidable challenge. Here, using extensive machine learning molecular dynamics, we demonstrate a fundamental thermal transport crossover in metal-organic frameworks. We reveal that grafting flexible side chains onto a pristine MOF backbone acts as a structural switch, strongly reducing the thermal conductivity by $\sim$70% (from $\sim 0.7$ to $\sim 0.2\ \text{W m}^{-1}\text{K}^{-1}$ at 300 K). Crucially, the functionalized derivatives exhibit a drastic transition from a classical Peierls $\sim 1/T$ decay to an anomalous, temperature-independent glass-like plateau. Reciprocal- and real-space analyses reveal the microscopic origins: the side chains act as built-in local resonators that trap acoustic energy via strong low-frequency resonant hybridization, while simultaneously inducing extreme steric crowding. Consequently, the heat-carrying phonon modes become critically damped, with their mean free paths strictly confined to the nanometer scale and their lifetimes collapsing to the Ioffe-Regel limit. This work establishes a highly programmable molecular engineering strategy to dismantle the phonon gas model, forcing crystalline frameworks into an extreme diffusive transport regime.

cond-mat.soft↗

arXiv2026

Modular hybrid machine learning and physics-based potentials for scalable modeling of van der Waals heterostructures

Accurately modeling the structural reconstruction and thermodynamic behavior of van der Waals (vdW) heterostructures remains a significant challenge due to the limitations of conventional force fields in capturing their complex mechanical, thermal, electronic, and tribological properties. To address these limitations, we develop a hybrid framework that combines single-layer machine-learned potential ($s$MLP) with physics-based anisotropic interlayer potential (ILP), effectively decoupling intralayer and interlayer interactions. This $s$MLP+ILP approach modularizes the modeling of vdW heterostructures like assembling LEGOs, reducing the required training configurations by at least an order of magnitude compared to the pure MLP approach, while retaining predictive accuracy and computational efficiency. We validate our framework by accurately reproducing the mechanical and thermal transport properties of graphite and bulk hexagonal boron nitride ($h$-BN), and by resolving intricate Moiré patterns in graphene/$h$-BN bilayer and graphene/graphene/$h$-BN trilayer heterostructures, achieving excellent agreement with experimental observations. Leveraging the developed $s$MLP+ILP approach, we reveal the stacking order-dependent formation of Moir{é} superlattice in trilayer graphene/$h$-BN/MoS$_2$ heterostructures, demonstrating its ability to accurately model large-scale vdW systems comprising hundreds of thousands of atoms with near $ab$ $initio$ precision. These findings demonstrate that the hybrid $s$MLP+ILP framework remarkably outperforms existing pure machine-learned or empirical potentials, offering a scalable and transferable solution for accurately and extensively modeling complex vdW materials across diverse applications, including sliding ferroelectricity, thermal management, resistive switching, and superlubric nanodevices.

physics.comp-ph↗

arXiv2025

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations

Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry to heat transport remains challenging. In this work, we develop a machine-learned neuroevolution potential (NEP) trained on an existing density functional theory dataset (\textit{Angew.\ Chem.\ Int.\ Ed.}, \textbf{63} , e202410088 (2024)), achieving reasonable accuracy at a computational cost much lower than the existing machine-learned and empirical potentials. Leveraging this potential, we perform large-scale molecular dynamics (MD) simulations to model the thermal reduction of GO across realistic structural domains. Using the homogeneous nonequilibrium MD method with a proper quantum-statistical correction scheme, we find that reduced GO exhibits strongly suppressed thermal conductivities, ranging from a few to tens of Wm$^{-1}$K$^{-1}$, substantially lower than pristine GO without defects and far below graphene. Moreover, the thermal conductivity of reduced GO increases moderately with increasing OH/O ratio, except at the highest oxidation level (O/C=0.5) where this trend inverts, while decreasing significantly with increasing O/C ratio, a trend strongly correlated with the fraction of recovered graphene-like structures. Our work provides a computationally tractable and predictive atomistic machine learning framework for exploring how chemical structure governs heat transport in heterogeneous carbon materials.

physics.chem-ph↗

arXiv2025

Anisotropic and isotropic elasticity and thermal transport in monolayer C$_{24}$ networks from machine-learning molecular dynamics

Two-dimensional fullerene networks have recently attracted increasing interest due to their diverse bonding topologies and mechanically robust architectures. In this work, we develop an accurate machine-learned potential NEP-C$_{24}$ for both the quasi-hexagonal phase (qHP) and the quasi-tetragonal phase (qTP) C$_{24}$ monolayers, based on the neuroevolution potential (NEP) framework. Using this NEP-C$_{24}$ model, we systematically investigate the elastic and thermal transport properties. Compared with C$_{60}$ monolayers, both C$_{24}$ phases exhibit markedly enhanced stiffness, arising from the combination of reduced molecular size and increased density of covalent bonds. The qTP C$_{24}$ monolayer shows nearly isotropic elastic properties and thermal conductivities along its two principal axes owing to its four-fold symmetry, whereas the chain-like, misaligned bonding topology of the qHP C$_{24}$ monolayer leads to pronounced in-plane anisotropy. Homogeneous nonequilibrium molecular dynamics and spectral decomposition analyses reveal that low-frequency ($<5$ THz) acoustic phonons dominate heat transport, with directional variations in phonon group velocity and mean free path governing the anisotropic response in qHP C$_{24}$. Real-space heat flow visualizations further show that, in these fullerene networks, phonon transport is dominated by strong inter-fullerene covalent bonds rather than weak van der Waals interactions. These findings establish a direct link between intermolecular bonding topology and phonon-mediated heat transport, providing guidance for the rational design of fullerene-based two-dimensional materials with tunable mechanical and thermal properties.

cond-mat.mtrl-sci↗

arXiv2025

Intrinsic Fracture Nonreciprocity at the Nanoscale

We reveal intrinsic fracture nonreciprocity, manifesting as directional asymmetry in crack resistance, in two-dimensional heterostructures engineered through lattice-mismatched interfaces. Density-functional theory combined with machine-learning molecular dynamics show that intrinsic lattice mismatch between bonded component crystals imprints asymmetric prestrain states at crack tips, governing bond-breaking thresholds through charge redistribution. The failure criterion obeys a universal exponential scaling law between normalized charge density and bond strain, insensitive to bonding chemistry and local atomic environment. The magnitude of nonreciprocity scales systematically with lattice mismatch, reaching 49% at 10% mismatch. Validation across hexagonal, square, rectangular, and oblique two-dimensional lattices confirms universality, establishing interface strain engineering as a general design principle that bridges electronic structure to nanoscale failure, enabling rational design of damage-tolerant nanostructures.

cond-mat.mtrl-sci↗

arXiv2025

Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics

Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we present an accurate and transferable machine-learned potential (MLP) for the tungsten-hydrogen binary system within the neuroevolution potential (NEP) framework, trained through active learning on extensive density functional theory data. The developed NEP-WH model reproduces a wide range of lattice and defect properties in tungsten systems, as well as hydrogen solubility, with near first-principles accuracy, while retaining the efficiency of empirical potentials. Crucially, it is the first MLP capable of capturing hydrogen trapping and H\textsubscript{2} formation in nanovoids, with quantitative fidelity. Large-scale machine-learning molecular dynamics simulations reveal a distinct aggregation pathway where planar hydrogen clusters nucleate and grow along \{100\} planes near voids, with hexagonal close-packed structures emerging at their intersections. Under uniaxial tension, these aggregates promote bubble fracture and the development of regular \{100\} cracks, suppressing dislocation activity and resulting in brittle fracture behavior. This work provides detailed atomistic insights into hydrogen bubble evolution and fracture in nanovoids, enables predictive modeling of structural degradation in extreme environments, and advances fundamental understanding of hydrogen-induced damage in structural metals.

cond-mat.mtrl-sci↗

arXiv2025

Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials

Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW) interactions. However, accurately modeling their thermal transport properties remains a significant challenge, primarily due to the computational limitations of density functional theory (DFT) and the inaccuracies of classical force fields in non-equilibrium regimes. To address this, we use a recently developed hybrid computational framework that combines machine learning potential (MLP) for intralayer interactions with registry-dependent interlayer potential (ILP) for anisotropic vdW interlayer interaction, achieving near quantum mechanical accuracy. This approach demonstrates exceptional agreement with DFT calculations and experimental data for TMD systems, accurately predicting key properties such as lattice constants, bulk modulus, moiré reconstruction, phonon spectra, and thermal conductivities. The scalability of this method enables accurate simulations of TMD heterostructures with large-scale moiré superlattices, making it a transformative tool for the design of TMD-based thermal metamaterials and devices, bridging the gap between accuracy and computational efficiency.

physics.comp-ph↗

arXiv2025

Interface phonon modes governing the ideal limit of thermal transport across diamond/cubic boron nitride interfaces

Understanding the ideal limit of interfacial thermal conductance (ITC) across semiconductor heterointerfaces is crucial for optimizing heat dissipation in practical applications. By employing a highly accurate and efficient machine-learned potential trained herein, we perform extensive non-equilibrium molecular dynamics simulations to investigate the ITC of diamond/cubic boron nitride ($c$BN) interfaces. The ideal diamond/$c$BN interface exhibits an unprecedented ITC of 11.0 $\pm$ 0.1 GW m$^{-2}$ K$^{-1}$, setting a new upper bound for heterostructure interfaces. This exceptional conductance originates from extended phonon modes due to acoustic matching and localized C-atom modes that propagate through B-C bonds. However, atomic diffusion across the ideal interface creates mixing layers that disrupt these characteristic phonon modes, substantially suppressing the thermal transport from its ideal limit. Our findings reveal how interface phonon modes govern thermal transport across diamond/$c$BN interfaces, providing insights for thermal management in semiconductor devices.

cond-mat.mtrl-sci↗

arXiv2025

AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential

Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we present AlphaNet, a local-frame-based equivariant model that simultaneously improves computational efficiency and predictive precision for interatomic interactions. By constructing equivariant local frames with learnable geometric transitions, AlphaNet encodes atomic environments with enhanced representational capacity, achieving state-of-the-art accuracy in energy and force predictions. Extensive benchmarks on large-scale datasets spanning molecular reactions, crystal stability, and surface catalysis (Matbench Discovery and OC2M) demonstrate its superior performance over existing neural network interatomic potentials while ensuring scalability across diverse system sizes with varying types of interatomic interactions. The synergy of accuracy, efficiency, and transferability positions AlphaNet as a transformative tool for modeling multiscale phenomena, decoding dynamics in catalysis and functional interfaces, with direct implications for accelerating the discovery of complex molecular systems and functional materials.

cs.LG↗

arXiv2025

Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures

Probing the ideal limit of interfacial thermal conductance (ITC) in two-dimensional (2D) heterointerfaces is of paramount importance for assessing heat dissipation in 2D-based nanoelectronics. Using graphene/hexagonal boron nitride (Gr/$h$-BN), a structurally isomorphous heterostructure with minimal mass contrast, as a prototype, we develop an accurate yet highly efficient machine-learned potential (MLP) model, which drives nonequilibrium molecular dynamics (NEMD) simulations on a realistically large system with over 300,000 atoms, enabling us to report the ideal limit range of ITC for 2D heterostructures at room temperature. We further unveil an intriguing stacking-sequence-dependent ITC hierarchy in the Gr/$h$-BN heterostructure, which can be connected to moiré patterns and is likely universal in van der Waals layered materials. The underlying atomic-level mechanisms can be succinctly summarized as energy-favorable stacking sequences facilitating out-of-plane phonon energy transmission. This work demonstrates that MLP-driven MD simulations can serve as a new paradigm for probing and understanding thermal transport mechanisms in 2D heterostructures and other layered materials.

physics.comp-ph↗

arXiv2025

Softening of Vibrational Modes and Anharmonicity Induced Thermal Conductivity Reduction in a-Si:H at High Temperatures

Hydrogenated amorphous silicon (a-Si:H) has garnered considerable attention in the semiconductor industry, particularly for its use in solar cells and passivation layers for high performance silicon solar cells, owing to its exceptional photoelectric properties and scalable manufacturing processes. A comprehensive understanding of thermal transport mechanism in a-Si:H is essential for optimizing thermal management and ensuring the reliable operation of these devices. In this study, we developed a neuroevolution machine learning potential based on first-principles calculations of energy, forces, and virial, which enables accurate modeling of interatomic interactions in both a-Si:H and a-Si systems. Using the homogeneous nonequilibrium molecular dynamics (HNEMD) method, we systematically investigated the thermal conductivity of a-Si:H and a-Si across a temperature range of 300-1000 K and hydrogen concentrations ranging from 6 to 12 at%. Our simulation results found that thermal conductivity of a-Si:H with 12 at% hydrogen was significantly reduced by 12% compared to that of a-Si at 300 K. We analyzed the spectral thermal conductivity, vibrational density of states and lifetimes of vibrational modes, and revealed the softening of vibrational modes and anharmonicity effects contribute to the reduction of thermal conductivity as temperature and hydrogen concentration increase. Furthermore, the influence of hydrogen concentration and temperature on diffuson and propagon contribution to thermal conductivity of a-Si:H was revealed. This study provides valuable insights for developing thermal management strategies in silicon-based semiconducting devices and advances the understanding of thermal transport in amorphous systems.

cond-mat.mtrl-sci↗

arXiv2025

Advances in modeling complex materials: The rise of neuroevolution potentials

Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding the physical and chemical properties of materials. In recent years, machine-learned potentials (MLPs), trained against first-principles calculations, have become a new paradigm in materials modeling as they provide a desirable balance between accuracy and computational cost. The neuroevolution potential (NEP) approach, implemented in the open-source GPUMD software, has emerged as a promising machine-learned potential, exhibiting impressive accuracy and exceptional computational efficiency. This review provides a comprehensive discussion on the methodological and practical aspects of the NEP approach, along with a detailed comparison with other representative state-of-the-art MLP approaches in terms of training accuracy, property prediction, and computational efficiency. We also demonstrate the application of the NEP approach to perform accurate and efficient MD simulations, addressing complex challenges that traditional force fields typically can not tackle. Key examples include structural properties of liquid and amorphous materials, chemical order in complex alloy systems, phase transitions, surface reconstruction, material growth, primary radiation damage, fracture in two-dimensional materials, nanoscale tribology, and mechanical behavior of compositionally complex alloys under various mechanical loadings. This review concludes with a summary and perspectives on future extensions to further advance this rapidly evolving field.

cond-mat.mtrl-sci↗

arXiv2024

NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties

Water's unique hydrogen-bonding network and anomalous properties pose significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across varied conditions. Although machine-learned potentials have advanced the prediction of individual properties, a unified computational framework capable of simultaneously capturing water's complex and subtle properties with high accuracy has remained elusive. Here, we address this challenge by introducing NEP-MB-pol, a highly accurate and efficient neuroevolution potential (NEP) trained on extensive many-body polarization (MB-pol) reference data approaching coupled-cluster-level accuracy, combined with path-integral molecular dynamics and quantum-correction techniques to incorporate nuclear quantum effects. This NEP-MB-pol framework reproduces experimentally measured structural, thermodynamic, and transport properties of water across a broad temperature range, achieving simultaneous, fast, and accurate prediction of self-diffusion coefficient, viscosity, and thermal conductivity. Our approach provides a unified and robust tool for exploring thermodynamic and transport properties of water under diverse conditions, with significant potential for broader applications across research fields.

physics.chem-ph↗

arXiv2024

Chemifriction and Superlubricity: Friends or Foes?

The mechanisms underlying chemifriction, i.e. the contribution of interfacial bonding to friction in defected twisted graphene interfaces are revealed using fully atomistic machine-learning molecular dynamics simulations. This involves stochastic events of consecutive bond formation and rupture, that are spatially separated but not necessarily independent. A unique shear-induced interlayer atomic transfer healing mechanism is discovered that can be harnessed to design a run-in procedure to restore superlubric sliding. This mechanism should be manifested as negative differential friction coefficients that are expected to emerge under moderate normal loads. A physically motivated phenomenological model is developed to predict the effects of chemifriction in experimentally relevant sliding velocity regimes. This allows us to identify a distinct transition between logarithmic increase and logarithmic decrease of frictional stress with increasing sliding velocity. While demonstrated for homogeneous graphene interfaces, a similar mechanism is expected to occur in other homogeneous or heterogeneous defected two-dimensional material interfaces.

cond-mat.mes-hall↗

arXiv2024

Phonon coherence and minimum thermal conductivity in disordered superlattice

Phonon coherence elucidates the propagation and interaction of phonon quantum states within superlattice, unveiling the wave-like nature and collective behaviors of phonons. Taking MoSe$_2$/WSe$_2$ lateral heterostructures as a model system, we demonstrate that the intricate interplay between wave-like and particle-like phonons, previously observed in perfect superlattice only, also occurs in disordered superlattice. By employing molecular dynamics simulation based on a highly accurate and efficient machine-learned potential constructed herein, we observe a non-monotonic dependence of the lattice thermal conductivity on the interface density in both perfect and disordered superlattice, with a global minimum occurring at relatively higher interface density for disordered superlattice. The counter-intuitive phonon coherence contribution can be characterized by the lagged self-similarity of the structural sequences in the disordered superlattice. Our findings extend the realm of coherent phonon transport from perfect superlattice to more general structures, which offers more flexibility in tuning thermal transport in superlattices.

cond-mat.mtrl-sci↗