arXiv subjects

Po-Yen Chen

Publications and source records attributed to Po-Yen Chen.

12 recordsLinked to original sources

arXiv2026

Atomistic origin and strain control of the finite-temperature dielectric response in BaTiO3

The dielectric response of BaTiO3 (BTO) varies strongly with temperature, yet its local atomic origin remains unclear. Using electric-field-coupled molecular dynamics with a machine-learning force field, we show that the permittivity tracks the field-induced angular redistribution of local Ti-O off-centering rather than its magnitude or mean polar angle. Temperature and biaxial strain modify the local structure differently, but both responses follow a common relation with the same orientational descriptor, providing a real-space counterpart to soft-mode behavior.

cond-mat.mtrl-sci↗

arXiv2026

Understanding Federated Learning Through the Lens of Mechanism Design: The Role of Data Heterogeneity

Federated learning (FL) requires effective incentive mechanisms to motivate data sharing and prevent strategic free-riding. Recent FL mechanisms such as the Shapley value mechanism M^Shap guarantee reciprocal fairness for agents. However, a complete analysis of how such mechanisms impact social optimality and individual rationality under realistic, standalone outside options remains unknown. In this paper, we address this gap by adapting the classical Externality mechanism M^E to the federated learning setting. We conduct a comparison of M^Shap and M^E across three dimensions: social optimality, individual rationality, and fairness/reciprocity. First, we establish that M^Shap generally does not maximize social welfare because its marginal incentives drive agents to over-contribute resources, while M^E maximizes social welfare by design. Second, we evaluate participation incentives through the individual rationality gap when considering agents' outside options as standalone training on their own data. We find that both mechanisms ensure individual rationality in homogeneous settings. We further show that under mild conditions, M^E maintains this guarantee under agent heterogeneity, whereas M^Shap does not. Third, we demonstrate that while M^Shap maintains perfect reciprocity by design, M^E generally does not, and only ensures that individual benefits match Shapley contributions at symmetric equilibria under homogeneity, as it sacrifices individual fairness to maximize collective welfare under heterogeneity. Empirical simulations validate our theoretical findings and illustrate a tradeoff between reciprocal fairness and social efficiency.

cs.GT↗

arXiv2026

Transient Detour and Cooperative Oxygen Exchange in the Polarization Switching of Ferroelectric Hf0.5Zr0.5O2

Hafnium zirconium oxide (HZO) has attracted significant attention as a core material for next-generation non-volatile memories due to its excellent ferroelectricity in the ultra-thin film regime and its CMOS process compatibility. However, the exploration of its polarization switching mechanism has predominantly relied on static energy barrier analyses, leaving the transient bond formation and cooperative dynamic mechanisms under actual electric field driving unresolved. In this study, we performed Electric-Field-Induced MD simulations on a defect-free ideal HZO lattice using a fine-tuned machine learning force field (MACEField). As a result, we successfully reproduced the P-E hysteresis loop dynamically and demonstrated that the polarization switching in HZO is driven not by conventional simple displacement models (S:N/S:T models), but by the dynamic mutual exchange of 3-coordinated oxygen (O3c) and 4-coordinated oxygen (O4c). Analysis of the oxygen atom displacement trajectories revealed that this pathway is accompanied by a unique "detour" behavior originating from transient cation-oxygen bond formation. Furthermore, we identified an "internal self-compensation mechanism" in which the local volumetric expansion and contraction accompanying the coordination number changes are effectively offset within the cell. These findings provide, from a dynamic perspective, a microscopic physical origin of for HZO's exceptional ability to sustain stable polarization switching without macroscopic strain, a property that has long distinguished HZO from conventional perovskite ferroelectrics yet lacked atomistic explanation. These findings suggest that preserving the integrity of cooperative O3c/O4c exchange pathways, rather than minimizing individual atomic displacements, is the key design principle for endurance and scalability in next-generation ferroelectric memories.

cond-mat.mtrl-sci↗

arXiv2026

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding

Prompt-based spoken language understanding (SLU) with large language models (LLMs) often suffers from inconsistent intent--slot structures due to decoding stochasticity, particularly in multi-intent scenarios. In view of this, we propose Semantic Frame-Level Multi-Task Self-Consistency (SFL-MTSC), a novel structured aggregation framework operating at the semantic frame level. Instead of output-level majority voting, SFL-MTSC decomposes predictions into intent-specific frames, applies domain--intent grouping and slot-level clustering, and evaluates cluster reliability using path support scoring. Reliable frames are retained and re-integrated to form the final prediction. Zero-shot experiments on the MAC-SLU benchmark dataset show improved slot F1 and overall accuracy over single-path inference, while intent accuracy remains largely stable across most settings.

cs.CL↗

arXiv2026

Transition from Homogeneous to Domain-Wall-Mediated Polarization Switching in BaTiO3: A Machine-Learning Molecular Dynamics Study

Polarization switching in ferroelectric BaTiO3 can proceed through fundamentally different mechanisms - yet the conditions that determine which pathway is realized remain poorly understood. Using machine-learning potential-based molecular dynamics with the MACEField model, we systematically vary supercell size to reveal a clear transition from homogeneous polarization switching to domain-wall-mediated switching, accompanied by a coercive field increase of over 50%. Shannon entropy analysis demonstrates that this transition is driven by size-dependent polarization fluctuations that promote 180 degree domain-wall nucleation - establishing a direct, quantitative link between local configurational disorder and macroscopic switching behavior. Furthermore, the switching pathway and hysteresis response are shown to depend critically on supercell geometry and the relative orientation of applied stress and electric field. These findings reveal that homogeneous and domain-wall-mediated switching are distinct physical regimes in BaTiO3, and that atomistic simulations must account for system size to correctly capture the operative switching mechanism.

cond-mat.mtrl-sci↗

arXiv2026

Long-range interaction effects on the phase transition, mechanical effect, and electric field response of BaTiO3 by machine learning potentials

Bulk materials are governed by both short-range and long-range interactions, both of which are naturally captured in conventional density functional theory (DFT) calculations through Ewald summation of electrostatic contributions. In contrast, machine learning potentials (MLPs) typically rely on local atomic environment descriptors, and long-range interactions are often neglected. Such approximations may introduce systematic energetic errors and lead to inaccuracies in predicted material properties. To systematically investigate the impact of long-range interactions in ferroelectric BaTiO3 within the framework of MLPs, we developed a long-range MACELES model and compared its performance with the previously reported BaTiO3 MACE model across four key properties (phonon dispersion, phase transition behavior, mechanical response, and ferroelectric properties including dielectric constants). We find that qualitative behaviors, including phase transitions, stress-induced polarization switching, and polarization-electric field hysteresis, are consistently reproduced by both models. In contrast, quantitative properties such as transition temperatures, elastic constants, and dielectric constants exhibit systematic improvements in MACELES model, highlighting the importance of incorporating long-range electrostatics for accurately describing the structural and dielectric responses of BaTiO3. These results suggest that while long-range interactions play a role in improving quantitative accuracy, their omission does not significantly alter the qualitative ferroelectric behavior of BaTiO3.

cond-mat.mtrl-sci↗

arXiv2026

Origin of Reduced Coercive Field in ScAlN: Synergy of Structural Softening and Dynamic Atomic Correlations

Among wurtzite-type ferroelectrics, scandium-doped aluminum nitride (ScAlN) has emerged as a leading candidate for CMOS-compatible low-voltage memory, combining strong spontaneous polarization with process compatibility. A remarkable feature of this system is the pronounced reduction of the coercive field (Ec) with increasing Sc concentration; however, its microscopic origin remains poorly understood at the atomic scale, particularly under finite temperature and applied electric fields. Here, we integrate a density-functional-theory-accurate machine-learning force field with an equivariant neural-network-based Born effective charge model to perform large-scale electric-field-driven molecular dynamics simulations at near-first-principles accuracy. The framework correctly reproduces the experimentally observed qualitative trends in key experimental trends, including the decrease in the c/a ratio and the monotonic reduction of Ec with increasing Sc content. Beyond static structural softening, we uncover a dynamic mechanism underlying Ec reduction. Sc atoms exhibit larger thermal vibrations and undergo preceding displacements during switching, acting as dynamic triggers for polarization reversal. Moreover, the displacement correlation between Sc and Al atoms evolves systematically with composition, enhancing cooperative atomic rearrangements and lowering the effective switching barrier. These results demonstrate that Ec reduction in ScAlN arises from the synergy of structural softening and dynamic correlation evolution, providing a new perspective for designing hexagonal ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2026

Decoupling structural and bonding effects on ferroelectric switching in ScAlN via molecular dynamics under an applied electric field

ScxAl1-xN has emerged as a promising wurtzite-type ferroelectric material, where increasing the Sc composition reduces both the coercive field (Ec) and remanent polarization (Pr). This composition-dependent behavior is physically attributed to two simultaneous changes: the increase in the internal structural parameter u (structural effect) and the weakening of bond strength (bonding effect). Because these factors are strongly coupled in experiments, their individual contributions to ferroelectric switching remain unclear. In this study, we systematically decoupled these effects using machine-learning force field-based molecular dynamics (MD) simulations under an applied electric field. By artificially tuning u via in-plane strain at a fixed composition, we demonstrated that Pr is determined exclusively by the structural effect, exhibiting a universal linear dependence regardless of the composition. In contrast, Ec deviated from this structural trend, implying an additional compositional contribution. To isolate this, we evaluated configurations with identical u but varying Sc compositions; Pr remained constant, whereas Ec systematically decreased due to bond weakening. Furthermore, static nudged elastic band (NEB) calculations revealed that the static switching barrier depends solely on u, failing to explicitly capture the bonding effect on Ec. These results establish that while Pr is governed strictly by the structural effect, Ec is determined by a superposition of structural and bonding effects. Our findings highlight the necessity of dynamic MD simulations for fully understanding ferroelectric switching in compositionally tunable materials.

cond-mat.mtrl-sci↗

arXiv2026

Effect of uniaxial compressive stress on polarization switching and domain wall formation in tetragonal phase BaTiO3 via machine learning potential

Ferroelectric materials such as BaTiO3 exhibit spontaneous polarization that can be reoriented by an external electric field, forming the basis of various memory, actuator, and sensor applications. The polarization switching behavior, however, is strongly influenced by mechanical boundary conditions due to the intrinsic electromechanical coupling in ferroelectrics. In this study, we employ a machine learning interatomic potential to investigate the effect of uniaxial compressive stress on polarization switching and domain wall evolution in the tetragonal phase of BaTiO3. This study revealed a critical stress about 120 MPa which 90 degree polarization switching occurs. Beyond the critical stress, larger supercells exhibit lower activation energies for polarization switching with 180-degree domain wall formation and weaker constraints from periodic boundary conditions, thereby facilitating domain-wall formation. Besides, Increasing compressive stress reduces both the remnant polarization and the coercive field, while a double hysteresis loop emerges at a stress level of 80 MPa. These findings provide atomistic insights into stress-controlled ferroelectric switching and highlight the crucial role of mechanical loading in designing reliable ferroelectric devices.

cond-mat.mtrl-sci↗

arXiv2025

Electric Field-Induced Phase Transitions and Hysteresis in Ferroelectric HfO2 Captured with Machine Learning Potential

Electric field-induced studies, including phase transition and polarization hysteresis, for ferroelectric HfO2 at the atomic scale are critical since they can largely affect its application in ferroelectric and dielectric devices. However, conventional first-principles approaches are computationally limited in capturing large-scale atomic dynamics under realistic field conditions. Here, to enable electric-field-driven molecular dynamics simulations, we develop a machine learning potential (MLP) tailored for HfO2, coupled with an in-situ Born effective charge (BEC) model. This framework enables us to capture key phenomena, including field-induced phase transitions, polarization switching, and strain-dependent dielectric responses, with high fidelity and computational efficiency. Notably, we reproduce hysteresis loops and phase transition barriers consistent with AIMD results and reveal possible electric-field-induced polarization activation in the monoclinic phase. Our approach offers a scalable and transferable tool for atomistic exploration of functional oxides and paves the way for data-driven design of ferroelectric devices.

cond-mat.mtrl-sci↗

arXiv2022

Reinforcement Learning for Resilient Power Grids

Traditional power grid systems have become obsolete under more frequent and extreme natural disasters. Reinforcement learning (RL) has been a promising solution for resilience given its successful history of power grid control. However, most power grid simulators and RL interfaces do not support simulation of power grid under large-scale blackouts or when the network is divided into sub-networks. In this study, we proposed an updated power grid simulator built on Grid2Op, an existing simulator and RL interface, and experimented on limiting the action and observation spaces of Grid2Op. By testing with DDQN and SliceRDQN algorithms, we found that reduced action spaces significantly improve training performance and efficiency. In addition, we investigated a low-rank neural network regularization method for deep Q-learning, one of the most widely used RL algorithms, in this power grid control scenario. As a result, the experiment demonstrated that in the power grid simulation environment, adopting this method will significantly increase the performance of RL agents.

cs.LG↗

arXiv2016

The Development Strategy of IT Capability: A Contingency Perspective

This study proposes a conceptual model to link IT capabilities, industry types, and value implications. We attempt to use a contingency analysis to theorize that which types of IT capabilities (e.g., externally-focused, internally-focused, and aggregate IT capability) should a firm develop and then what benefits (e.g., firm value and firm performance) it will gain according to its industry's value creation logic (e.g., value chain-based, value shop-based, and value network-based industry). The empirical findings show that a value network-based firm should develop externally-focused IT capabilities to create its firm value and a value chain-based firm should develop aggregate IT capabilities to improve its firm performance and create its firm value.

cs.CY↗