arXiv subjects

Zhicheng Zhong

Publications and source records attributed to Zhicheng Zhong.

At least 19 recordsLinked to original sources

arXiv2026

Chemical-space completeness through iterative crystal-structure generation and model adaptation

The emergence of deep learning has brought large-scale exploration of crystalline materials closer to practical realization. Yet, universal atomistic models face a practical trade-off between chemical generality and the accuracy, efficiency, and adaptability required for intensive exploration of specific materials systems. Within a bounded chemical system, this trade-off can be relaxed by exploiting its limited chemical complexity. Guided by this intuition, we propose a chemical-system-centric strategy that couples crystal-structure generative models with machine-learned force fields (MLFFs) in an iterative generation-evaluation-refinement loop. Using Li--P--S as a test case, we generate approximately 70,000 candidate structures, including more than 10,000 stable-unique-novel structures. The diversity of near-equilibrium local environments saturates within the first few iterations, accompanied by convergence of MLFF prediction errors, providing an operational measure of chemical-space completeness within bounded systems. The exploration also recovers chemically plausible P--S motifs that are absent from the pretraining databases but supported by earlier experiments. The resulting system-adapted models and structures further enable finite-$P$--$T$ phase-stability calculations, Li-ion transport screening, and electronic-structure prediction. These results suggest that chemical-system-centric exploration provides a practical route toward data-efficient and high-fidelity modeling within bounded chemical spaces.

cond-mat.mtrl-sci↗

arXiv2026

Neural-Network Solutions to Real-Space Charge Density and Generalization

The Hohenberg-Kohn theorem establishes that, in principle, the ground state (GS) charge density contains all GS information of a many-electron system, such that all GS observables can be expressed as functionals of the GS charge density. Conventional Kohn-Sham density functional theory requires iterative solution of the self-consistent-field equations at substantial computational cost, motivating the development of deep learning surrogates for electronic structure calculations and, in turn, accelerating computer-aided materials design. Here, we propose AIDEN, an Atomic-Interaction Density Equivariant Network for solving real-space charge density. AIDEN separates the element-dependent one-center density from environment-induced density redistribution and represents the latter through complementary atom- and edge-centered tensor correlations. A continuous low-rank Gaussian decoder then reconstructs the density at arbitrary spatial coordinates while reusing atomic encodings independently of the evaluation grid. AIDEN achieves state-of-the-art accuracy on periodic crystal benchmarks while remaining competitive for molecular systems, and further demonstrates zero-shot transferability across several structurally distinct out-of-distribution case studies. Furthermore, AIDEN provides substantially faster inference than both baseline models and full SCF calculations, enabling efficient charge density reconstruction for large-scale electronic structure calculations.

cond-mat.mtrl-sci↗

arXiv2026

Dynamics-Informed Reinforcement Learning for Agile and Energy-Efficient Locomotion of a Monopedal Hopping Quadcopter

Although aerial-legged robots offer combined agility and efficiency, controlling high-speed hopping under complex hybrid dynamics is challenging. Reinforcement Learning (RL) is promising but prone to energy-inefficient "reward hacking". We propose a Dynamics-Informed RL framework for a monopedal hopping quadcopter. By embedding a target Specific Energy into the reward, we constrain the optimization to a physically viable energy manifold, ensuring stable hopping behaviour. By rewarding the phase-consistent behavior, it can encourage bio-inspired stance-phase impulse. Furthermore, penalizing the electro-mechanical power waste induces the motors generate an efficient impulse. This enables the policy to inject energy strictly during spring restitution without heuristic state machines. MuJoCo simulations validate robust height regulation and forward velocity tracking up to 2.0 m/s despite severe attitude-contact coupling. Ultimately, our approach yields a highly agile hopping gait, reducing energy consumption by 82% and 73% compared to hovering baselines and inefficiency baseline, respectively.

cs.RO↗

arXiv2026

Learning to Exploit Passive Dynamics for Energy-Efficient Target Hopping of a Spring-Legged Quadcopter

Combining aerial thrust with spring-loaded hopping makes monopedal quadcopters promising for locomotion over complex terrain, but heuristic proportional-integral-derivative (PID) tuning limits coordination between active thrust and passive contact dynamics. We present a direct estimated-state-to-motor Proximal Policy Optimization (PPO) policy that commands four motors without an explicit hopping state machine or low-level attitude PID. Its reward combines Energy-Manifold Shaping for mass-normalized vertical-energy tracking and apex-state anchoring with Efficiency Shaping, which uses a history-aware power estimator to penalize general power use, impose an additional airborne-power cost, and penalize airborne near-stationarity. In representative hardware runs, the PPO-based control stack reduced cycle-averaged measured electrical power by 30.7% and mean total normalized thrust by 49.8% relative to the tuned PID-based control stack, while retaining repeatable commanded-height hopping and more concentrated landings. These observations are consistent with improved use of passive dynamics and reduced measured electrical demand.

cs.RO↗

arXiv2026

Data-Efficient Adaptation of DPA-4 Force Fields to DFT+U Energetics: A Case Study in NiO

Foundation machine-learned force fields (MLFFs) are often pretrained on broad materials datasets whose electronic-structure conventions may not reproduce the phase energetics required for a specific correlated material. Using NiO as a case study, we examine whether incorrect source-level phase energetics can be corrected efficiently through target-level fine-tuning. Along a common structural interpolation, non-spin-polarized PBE and ferromagnetic PBE+U predict opposite energetic orderings of the octahedral Oct and square-planar Sqr phases. Pretrained DPA-4 models adapt rapidly to the NiO PBE+U surface, reaching energy and force root-mean-square errors (RMSEs) of approximately 0.5 meV/atom and 30 meV/Å, respectively, with approximately 170 PBE+U labels. Crucially, models previously fine-tuned to the opposing no-U surface recover the qualitative PBE+U phase ordering with nearly the same target-data efficiency as models fine-tuned directly from their respective pretrained initializations. Our results show that incorrect source-level phase energetics can be reversed through target-level fine-tuning, and suggest a practical multi-fidelity strategy in which pretraining prioritizes broad, consistent, and affordable data, while compact target-level datasets impose energetics through application-specific fine-tuning.

cond-mat.mtrl-sci↗

arXiv2026

Neural Gauge-P Representation for Open Quantum Dynamics of Interacting Bosons

Simulating the nonequilibrium dynamics of interacting open quantum systems remains challenging beyond small system sizes. Quantum phase-space representations provide a scalable approach, but their useful simulation time can be limited by broad distribution tails and the associated boundary terms. We introduce the neural gauge-$P$ representation for open bosonic systems, in which stochastic gauges are parameterized by neural networks and optimized using exact moment equation residuals. For the driven-dissipative Bose--Hubbard model in both single-site and square-lattice settings, the neural gauge-$P$ representation remains accurate during long-time evolution toward the steady state, whereas the corresponding ungauged representation becomes unreliable at substantially earlier times. These results demonstrate the potential of the neural gauge-$P$ representation for accurate simulations of nonequilibrium open quantum many-body dynamics.

quant-ph↗

arXiv2026

Temperature-driven structural phase transitions in SmNiO$_3$: insights from deep potential molecular dynamics simulations

The metal-insulator transition (MIT) in rare-earth nickelates exemplifies the intricate coupling between lattice dynamics and electronic effects. This strong interplay makes it challenging to disentangle their individual roles in driving the transition in RNiO3. Here, we isolate the structure response from electronic effect by employing molecular dynamics (MD) simulations based on a machine-learned interatomic potential. Taking SmNiO3 as a prototypical system, our simulations show that the structural phase transition is intrinsically temperature-driven and occurs spontaneously via collective lattice distortions. The simulated critical temperature is 340 K and can be further tuned by pressure. These findings provide atomistic insights into the understanding of structural evolution in triggering the phase transition and hence the MIT in RNiO3.

cond-mat.mtrl-sci↗

arXiv2026

AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials

Large language models (LLMs) have shown promising potential in scientific research, enabling tasks ranging from knowledge retrieval to property prediction. Existing science benchmarks mainly focus on perceptual or knowledge-based tasks, largely ignoring the modelling tasks, a fundamental starting point for any real scientific research. For materials science, constructing and manipulating atomic structures is one of the most creative and least automated steps. In this work, we introduce AtomWorld, a benchmark designed to evaluate the abilities of LLMs on structure modifications. The benchmark includes ten fundamental actions under four widely used modelling categories, enabling verifiable evaluation metrics. We find that Claude Opus 4.6 generally performs the best. While the success rate decreases markedly with increasing modelling complexity, with particularly low success rates (below 12\% for rotation) for operations involving complex spatial relations. Our results suggest that contemporary LLMs are better suited as copilots for materials structure modelling rather than fully unsupervised autonomous scientific agents. Beyond evaluation, AtomWorld also serves as a testbed and playground for developing future structure-aware models, including reinforcement learning and agentic approaches.

cond-mat.mtrl-sci↗

arXiv2026

LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-timestamp 3D Human Pose Estimation

Several methods have been proposed to estimate 3D human pose from multi-view images, achieving satisfactory performance on public datasets collected under relatively simple conditions. However, there are limited approaches studying extracting 3D human skeletons from multimodal inputs, such as RGB and point cloud data. To address this gap, we introduce LiCamPose, a pipeline that integrates multi-view RGB and sparse point cloud information to estimate robust 3D human poses via single frame. We demonstrate the effectiveness of the volumetric architecture in combining these modalities. Furthermore, to circumvent the need for manually labeled 3D human pose annotations, we develop a synthetic dataset generator for pretraining and design an unsupervised domain adaptation strategy to train a 3D human pose estimator without manual annotations. To validate the generalization capability of our method, LiCamPose is evaluated on four datasets, including two public datasets, one synthetic dataset, and one challenging self-collected dataset named BasketBall, covering diverse scenarios. The results demonstrate that LiCamPose exhibits great generalization performance and significant application potential. The code, generator, and datasets will be made available upon acceptance of this paper.

cs.CV↗

arXiv2025

Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models

Universal force fields generalizable across the periodic table represent a new trend in computational materials science. However, the applications of universal force fields in material simulations are limited by their slow inference speed and the lack of first-principles accuracy. Instead of building a single model simultaneously satisfying these characteristics, a strategy that quickly generates material-specific models from the universal model may be more feasible. Here, we propose a new workflow pattern, PFD (Pre-training, Fine-tuning, and Distillation), which automatically generates machine-learning force fields for specific materials from a pre-trained universal model through fine-tuning and distillation. By fine-tuning the pre-trained model, our PFD workflow generates force fields with first-principles accuracy while requiring one to two orders of magnitude less training data compared to traditional methods. The inference speed of the generated force field is further improved through distillation, meeting the requirements of large-scale molecular simulations. Comprehensive testing across diverse materials including complex systems, such as amorphous carbon, interface, etc., reveals marked enhancements in training efficiency, which suggests the PFD workflow a practical and reliable approach for force field generation in computational material sciences.

cond-mat.mtrl-sci↗

arXiv2025

Dual instability of superconductivity from oxygen defects in La$_3$Ni$_2$O$_{7+δ}$

We uncover a dual mechanism by which oxygen defects suppress superconductivity in the bilayer nickelate La$_3$Ni$_2$O$_{7+δ}$ using density functional theory, dynamical mean-field theory, and functional renormalization group analysis. Apical vacancies and interbilayer interstitials emerge as the dominant low-energy defect species and are further stabilized by orthorhombic domain walls. These two defect classes drive the electronic structure in opposing directions. Vacancy-induced disorder generates local magnetic moments and promotes Anderson localization at moderate concentrations, whereas periodic interstitial ordering yields a coherent but weakly correlated metallic background that fails to support superconductivity. These findings highlight the decisive role of oxygen defects in shaping the superconducting and provide microscopic guidance for improving superconductivity through controlled defect engineering.

cond-mat.supr-con↗

arXiv2025

Neural network impurity solver for real-frequency dynamical mean-field theory

We introduce a neural network impurity solver for real-frequency DMFT that employs a multihead cross-attention mechanism to map hybridization functions to spectral functions, conditioned on impurity parameters. Trained on high-quality MPS data from complex contour time evolution and incorporating derivative constraints with respect to the complex-time angle, our model achieves smooth generalization to the real-frequency axis. Benchmarking on the single-band Hubbard model for the Bethe lattice demonstrates quantitative accuracy across metallic, strongly correlated, and insulating regimes.

cond-mat.str-el↗

arXiv2025

EAC-Net: Predicting real-space charge density via equivariant atomic contributions

Charge density is central to density functional theory (DFT), as it fully defines the ground-state properties of a material system. Obtaining it with high accuracy is a computational bottleneck. Existing machine learning models are constrained by trade-offs among accuracy, efficiency, and generalization. Here, we introduce the Equivariant Atomic Contribution Network (EAC-Net), which couples atoms and grids to integrate the strengths of grid-based and basis-function frameworks. EAC-Net achieves high accuracy (typically below 1% error), enhanced efficiency, and strong generalization across complex systems. Building on this framework, we develop EAC-mp, a universal charge density model covering the periodic table. The model demonstrates robust zero-shot performance across diverse systems, and generalizes beyond the training distribution, supporting downstream applications such as band structure calculations. By linking local chemical environments to charge densities, EAC-Net provides a scalable framework for accelerating electronic structure prediction and enabling high-throughput materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Intrinsic Strain-Driven Topological Evolution in SrRuO3 via Flexural Strain Engineering

Strain engineering offers a powerful route to tailor topological electronic structures in correlated oxides, yet conventional epitaxial strain approaches introduce extrinsic factors such as substrate-induced phase transitions and crystalline quality variations, which makes the unambiguous identification of the intrinsic strain effects challenging. Here, we develop a flexural strain platform based on van der Waals epitaxy and flexible micro-fabrication, enabling precise isolation and quantification of intrinsic strain effects on topological electronic structures in correlated oxides without extrinsic interference. Through strain-dependent transport measurements of the Weyl semimetal SrRuO3, we observed a significant enhancement of anomalous Hall conductivity by 21% under a tiny strain level of 0.2%, while longitudinal resistivity remains almost constant -- a hallmark of intrinsic topological response. First-principles calculations reveal a distinct mechanism where strain-driven non-monotonic evolution of Weyl nodes across the Fermi level, exclusively governed by lattice constant modulation, drives the striking AHC behavior. Our work not only highlights the pivotal role of pure lattice strain in topological regulation but also establishes a universal platform for designing flexible topological oxide devices with tailored functionalities.

cond-mat.mtrl-sci↗

arXiv2025

Mechanism of Anisotropic Crystallization and Phase Transitions under Van der Waals Squeezing

Mechanical confinement strategies, such as van der Waals (vdW) squeezing, have emerged as promising routes for synthesizing non-vdW two-dimensional (2D) layers, surprisingly yielding high-quality single crystals with lateral sizes approaching 100 micrometer. However, the underlying mechanisms by which such a straightforward approach overcomes the long-standing synthesis challenges of non-vdW 2D materials remains a puzzle. Here, we investigate the crystallization dynamics and phase evolution of Bi under vdW confinement through molecular dynamics (MD) simulations powered by a machine-learning force filed fine-tuned and distilled from a pre-trained model with DFT-level accuracy. We reveal that pressure-dependent layer modulation arises from a quantum confinement-driven anisotropic crystallization mechanism, in which out-of-plane layering occurs nearly two orders of magnitude faster than in-plane ordering. Two critical transitions are identified: an alpha-to-beta phase transformation at 1.64 GPa, and a subsequent collapse into a single-atomic layer at 2.19 GPa. The formation of large-area single crystals is enabled by substrate-induced orientational selection and accelerated grain boundary migration, driven by atomic diffusion at elevated temperatures. These findings resolve the mechanistic origin of high-quality 2D crystal growth under confinement and establish guiding principles for the controlled synthesis of metastable 2D single crystals, with implications for next-generation quantum and nanoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Uncovering coupled ionic-polaronic dynamics and interfacial enhancement in Li$_x$FePO$_4$

Understanding and controlling coupled ionic-polaronic dynamics is crucial for optimizing electrochemical performance in battery materials. However, studying such coupled dynamics remains challenging due to the intricate interplay between Li-ion configurations, polaron charge ordering, and lattice vibrations. Here, we develop a fine-tuned machine-learned force field (MLFF) for Li$_x$FePO$_4$ that captures coupled ion-polaron behavior. Our simulations reveal picosecond-scale polaron flips occurring orders of magnitude faster than Li-ion migration, featuring strong correlation to Li configurations. Notably, polaron charge fluctuations are further enhanced at Li-rich/Li-poor phase boundaries, suggesting a potential interfacial electronic conduction mechanism. These results demonstrate the capability of fine-tuned MLFFs to resolve complex coupled transport and provide insight into emergent ionic-polaronic dynamics in multivalent battery cathodes.

cond-mat.mtrl-sci↗

arXiv2025

T-MSD: An improved method for ionic diffusion coefficient calculation from molecular dynamics

Ionic conductivity is a critical property of solid ionic conductors, directly influencing the performance of energy storage devices such as batteries. However, accurately calculating ionic conductivity or diffusion coefficient remains challenging due to the complex, dynamic nature of ionic motion, which often yield significant deviations, especially at room temperature. In this study, we propose an improved method, T-MSD, to enhance the accuracy and reliability of diffusion coefficient calculations. Combining time-averaged mean square displacement analysis with block jackknife resampling, this method effectively addresses the impact of rare, anomalous diffusion events and provides robust statistical error estimates from a single simulation. Applied to large-scale deep-potential molecular dynamics simulations, we show that T-MSD eliminates the need for multiple independent simulations while ensuring accurate diffusion coefficient calculations across systems of varying sizes and simulation durations. This approach offers a practical and reliable framework for precise ionic conductivity estimation, advancing the study and design of high-performance solid ionic conductors.

cond-mat.mtrl-sci↗

arXiv2025

Undamped Soliton-like Domain Wall Motion in Sliding Ferroelectrics

Sliding ferroelectricity in bilayer van der Waals materials exhibits ultrafast switching speed and fatigue resistance during the polarization switching, offering an avenue for the design of memories and neuromorphic devices. The unique polarization switching behavior originates from the distinct characteristics of domain wall (DW), which possesses broader width and faster motion compared to conventional ferroelectrics. Herein, using machine-learning-assisted molecular dynamics simulations and field theory analysis, we predict an undamped soliton-like DW motion in sliding ferroelectrics. It is found that the DW in sliding ferroelectric bilayer 3R-MoS2 exhibits uniformly accelerated motion under an external field, with its velocity ultimately reaches the relativistic-like limit due to continuous acceleration. Remarkably, the DW velocity remains constant even after the external field removal, completely deviating from the velocity breakdown observed in conventional ferroelectrics. This work provides opportunities for applications of sliding ferroelectrics in memory devices based on DW engineering.

cond-mat.mtrl-sci↗