arXiv subjects

Lingyao Zhang

Publications and source records attributed to Lingyao Zhang.

6 recordsLinked to original sources

arXiv2026

Significance of the dispersion force for ferroelectric switching in ZnO and related materials

Wurtzite-ZnO is a wide-bandgap polar material with a ferroelectric-switching barrier that is too high to utilize, but the barrier can be reduced and switching observed in substituted materials such as Zn0.5Mg0.5O. Here, we seek to understand atomic-scale features that control concerted polarization switching in these and related systems, focusing on the planar hexagonal structures h-ZnO and Zn0.5Mg0.5O that may act as metastable intermediate phases along the switching pathway. Consensus is obtained by considering a range of pure and dispersion-corrected density-functional theory (DFT) computational approaches, as well as ab initio Hartree-Fock (HF), M{\o}ller-Plesset perturbation-theory (MP2), and random-phase approximation (RPA) calculations. The perceived stability of h-ZnO is found to be strongly influenced by the dispersion correction, with the consensus being that dispersion interactions are insufficient to stabilize h-ZnO as a metastable phase in infinite crystals. In contrast, h-Zn0.5Mg0.5O is consistently predicted to be at least metastable, with some dispersion-corrected DFT approaches predicting it to be more stable than its wurtzite form; all DFT methods overestimate its stability compared to MP2 and RPA. Dispersion forces are found to be most significant for hypothetical planar hexagonal structures constrained to the lattice vectors of the wurtzite phases. In general, our results demonstrate that an accurate treatment of dispersion forces is essential when describing polarization switching and ferroelectric behavior in wurtzite-structured materials.

cond-mat.mtrl-sci↗

arXiv2025

Re-refinement of the structure of the planar hexagonal phase of ZnO nanocrystals

The planar hexagonal phase of ZnO, known as h-ZnO, g-ZnO, {\alpha}-ZnO, the Bk structure, the 5-5 phase, the {\alpha}-BN phase, etc., has P63/mmc symmetry and is implicated in ferroelectric switching mechanisms for wurtzite-ZnO. It is well-known in thin films on substrates and to be stabilized by external pressure, but critical is its possible existence in high-purity nanocrystals under ambient conditions. Indeed, a crystal structure has been reported, but this work remains controversial as first-principles calculations predict very different structural properties. Herein, the original experimental data is re-refined, through phase-shift determination and Morlet wavelet transformation, that molecular dynamics simulations associate with a P63/mmc structure with lattice parameters at room temperature of a = 3.45{\pm}0.02 {\AA} and c = 4.46{\pm}0.02 {\AA}. These values are 0.35 {\AA} and 0.80 {\AA}, respectively, larger than those previously reported and in good agreement with computational predictions. This confirms that ZnO nanocrystals can form a metastable planar hexagonal phase. It provides key information pertaining to polarization switching in ZnO, its derivatives, and general wurtzite-structured materials.

cond-mat.mtrl-sci↗

arXiv2024

Data Interpreter: An LLM Agent For Data Science

Large Language Model (LLM)-based agents have shown effectiveness across many applications. However, their use in data science scenarios requiring solving long-term interconnected tasks, dynamic data adjustments and domain expertise remains challenging. Previous approaches primarily focus on individual tasks, making it difficult to assess the complete data science workflow. Moreover, they struggle to handle real-time changes in intermediate data and fail to adapt dynamically to evolving task dependencies inherent to data science problems. In this paper, we present Data Interpreter, an LLM-based agent designed to automatically solve various data science problems end-to-end. Our Data Interpreter incorporates two key modules: 1) Hierarchical Graph Modeling, which breaks down complex problems into manageable subproblems, enabling dynamic node generation and graph optimization; and 2) Programmable Node Generation, a technique that refines and verifies each subproblem to iteratively improve code generation results and robustness. Extensive experiments consistently demonstrate the superiority of Data Interpreter. On InfiAgent-DABench, it achieves a 25% performance boost, raising accuracy from 75.9% to 94.9%. For machine learning and open-ended tasks, it improves performance from 88% to 95%, and from 60% to 97%, respectively. Moreover, on the MATH dataset, Data Interpreter achieves remarkable performance with a 26% improvement compared to state-of-the-art baselines. The code is available at https://github.com/geekan/MetaGPT.

cs.AI↗

arXiv2020

Map-Adaptive Goal-Based Trajectory Prediction

We present a new method for multi-modal, long-term vehicle trajectory prediction. Our approach relies on using lane centerlines captured in rich maps of the environment to generate a set of proposed goal paths for each vehicle. Using these paths -- which are generated at run time and therefore dynamically adapt to the scene -- as spatial anchors, we predict a set of goal-based trajectories along with a categorical distribution over the goals. This approach allows us to directly model the goal-directed behavior of traffic actors, which unlocks the potential for more accurate long-term prediction. Our experimental results on both a large-scale internal driving dataset and on the public nuScenes dataset show that our model outperforms state-of-the-art approaches for vehicle trajectory prediction over a 6-second horizon. We also empirically demonstrate that our model is better able to generalize to road scenes from a completely new city than existing methods.

cs.LG↗

arXiv2019

Developing Creative AI to Generate Sculptural Objects

We explore the intersection of human and machine creativity by generating sculptural objects through machine learning. This research raises questions about both the technical details of automatic art generation and the interaction between AI and people, as both artists and the audience of art. We introduce two algorithms for generating 3D point clouds and then discuss their actualization as sculpture and incorporation into a holistic art installation. Specifically, the Amalgamated DeepDream (ADD) algorithm solves the sparsity problem caused by the naive DeepDream-inspired approach and generates creative and printable point clouds. The Partitioned DeepDream (PDD) algorithm further allows us to explore more diverse 3D object creation by combining point cloud clustering algorithms and ADD.

cs.LG↗

arXiv2018

Hallucinating Point Cloud into 3D Sculptural Object

Our team of artists and machine learning researchers designed a creative algorithm that can generate authentic sculptural artworks. These artworks do not mimic any given forms and cannot be easily categorized into the dataset categories. Our approach extends DeepDream from images to 3D point clouds. The proposed algorithm, Amalgamated DeepDream (ADD), leverages the properties of point clouds to create objects with better quality than the naive extension. ADD presents promise for the creativity of machines, the kind of creativity that pushes artists to explore novel methods or materials and to create new genres instead of creating variations of existing forms or styles within one genre. For example, from Realism to Abstract Expressionism, or to Minimalism. Lastly, we present the sculptures that are 3D printed based on the point clouds created by ADD.

cs.AI↗