arXiv subjects

Xiaoya Chang

Publications and source records attributed to Xiaoya Chang.

5 recordsLinked to original sources

arXiv2026

Nuclearity of Copper Clusters on hBN/SiC Heterostructure Modulates Molecular Adsorption

Defect engineering can transform inert two-dimensional (2D) materials into chemically active and electronically tunable platforms by creating anchoring sites for metal atoms and clusters. Nevertheless, precise control over the formation, thermodynamic and kinetic stability, electronic structure, and chemical reactivity of metal species confined at these defect sites remains a challenge. Here, we use density functional theory (DFT) calculations assisted by machine-learning molecular dynamics (MLMD) simulations to elucidate the stability, electronic structure, and reactivity of Cu clusters anchored at boron vacancies (VB) in hBN/SiC heterostructures. Systematic variation of the Cu-to-vacancy ratio reveals a transition from isolated Cu atoms to multiatom Cu clusters at VB sites, with cluster growth reshaping the stability, electronic structure, and surface reactivity. Our results show that a single VB defect can be passivated by three Cu atoms, which compensate the local charge deficiency and stabilize the defect through Cu-N coordination. Capturing further Cu introduces localized midgap states that could influence the reactivity of the Cu-decorated defect sites. We probe the response of the Cu-decorated surface to chemically relevant gases CO, H2, O2, N2, H2S, and CO2, revealing implications for surface reactivity and stability. The calculations show pronounced cluster-size-dependent reactivity of Cu clusters at VB sites, with CO forming strong Cu-C bonds and O2 undergoing enhanced adsorption and molecular activation. Overall, this work identifies defect-engineered hBN/SiC as a versatile 2D platform for stabilizing Cu clusters and tuning gas-surface reactivity. By correlating Cu nuclearity at VB sites with electronic structure, molecular activation, and environmental robustness, our findings provide design guidelines for nuclearity-dependent metal functionalization of 2D heterostructures.

cond-mat.mtrl-sci↗

arXiv2026

Dislocation-loop formation is a first-order phase transition

Dislocation loops are the elementary product of radiation damage in crystals, limiting reactor-component lifetimes, power-electronics reliability and the coherence of solid-state qubits. Their nucleation has been simulated for six decades but never reduced to a thermodynamic law. We show that dislocation-loop formation is a \emph{first-order phase transition}, and construct its Ginzburg--Landau free energy, with the loop area as order parameter, entirely from atomistic simulation. In diamond, carbon self-interstitials condense into planar precursors that collapse abruptly into a prismatic $\tfrac{1}{2}\langle110\rangle$ loop across a 3.7-electronvolt barrier, with pressure--volume work supplying only 2\% of the energy released. The reduced free energy proves material-independent: the vacancy platelet-to-loop collapse in body-centred-cubic iron falls on the same one-parameter family, placing loop nucleation on a transferable thermodynamic footing.

cond-mat.mtrl-sci↗

arXiv2026

TorchNEP: Ultra-Efficient and Accurate Training of Neuroevolution Potentials

Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training strategy remains computationally demanding, limiting systematic exploration of model architectures and training protocols. Here, we present TorchNEP, a PyTorch-based implementation of NEP that combines analytically derived gradients, adaptive optimization, and a two-stage training strategy. TorchNEP accelerates training by more than two orders of magnitude while maintaining full compatibility with existing NEP models. We further show that the improvement in predictive accuracy primarily originates from the two-stage training protocol rather than the optimization algorithm itself. Across diverse benchmark datasets, TorchNEP consistently improves force and stress predictions while maintaining comparable or improved energy accuracy. Benchmark evaluations on elemental and alloy systems demonstrate enhanced predictive performance for both atomic configurations and key materials properties. Furthermore, we show that increasing model complexity does not necessarily improve predictive performance despite reducing training errors. Overall, TorchNEP provides an efficient and flexible training framework for developing more accurate and robust machine-learned interatomic potentials.

physics.comp-ph↗

arXiv2026

Machine-learned prediction of carbon interstitial clusters in diamond

Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form new defects whose configurational landscape is poorly charted, as subtle energy differences govern the competing minima and pathways. Here we build an interstitial-focused dataset by active learning and benchmark three machine-learning interatomic potentials -- GAP, NEP and the equivariant MACE -- against density functional theory for energies, forces and migration barriers. MACE reproduces the reference energetics and relative stabilities, whereas the others can misorder the ground states. Annealing molecular dynamics with the validated potentials uncovers a series of previously unreported carbon interstitial clusters, from di- to octa-interstitials -- several introducing in-gap states of interest as colour centres -- and shows that their metastability is governed by kinetically accessible pathways rather than energetic ordering. These results chart the interstitial defect landscape and accelerate defect discovery for quantum technologies.

physics.comp-ph↗

arXiv2025

A General Neural Network Potential for Energetic Materials with C, H, N, and O elements

The discovery and optimization of high-energy materials (HEMs) are constrained by the prohibitive computational expense and prolonged development cycles inherent in conventional approaches. In this work, we develop a general neural network potential (NNP) that efficiently predicts the structural, mechanical, and decomposition properties of HEMs composed of C, H, N, and O. Our framework leverages pre-trained NNP models, fine-tuned using transfer learning on energy and force data derived from density functional theory (DFT) calculations. This strategy enables rapid adaptation across 20 different HEM systems while maintaining DFT-level accuracy, significantly reducing computational costs. A key aspect of this work is the ability of NNP model to capture the chemical activity space of HEMs, accurately describe the key atomic interactions and reaction mechanisms during thermal decomposition. The general NNP model has been applied in molecular dynamics (MD) simulations and validated with experimental data for various HEM structures. Results show that the NNP model accurately predicts the structural, mechanical, and decomposition properties of HEMs by effectively describing their chemical activity space. Compared to traditional force fields, it offers superior DFT-level accuracy and generalization across both microscopic and macroscopic properties, reducing the computational and experimental costs. This work provides an efficient strategy for the design and development of HEMs and proposes a promising framework for integrating DFT, machine learning, and experimental methods in materials research. (To facilitate further research and practical applications, we open-source our NNP model on GitHub: https://github.com/MingjieWen/General-NNP-model-for-C-H-N-O-Energetic-Materials.)

cond-mat.mtrl-sci↗