arXiv subjects

Lei Zhuang

Publications and source records attributed to Lei Zhuang.

10 recordsLinked to original sources

arXiv2026

Extreme mixing enables broadband high-temperature electromagnetic absorption in high-entropy perovskites

Developing oxide absorbers with broadband high-temperature electromagnetic absorption performance has long been desirable in the electromagnetic absorption field, yet their absorption performance has remained unsatisfactory. Here, through an extreme mixing strategy, we successfully develop an ultrabroad effective absorption bandwidth (EAB) of 9.3 GHz in high-entropy perovskites up to 600 degree celsius, surpassing the previously reported oxide absorbers. Specifically, we achieve the extreme mixing in high-entropy perovskites by the successful incorporation of up to 13 cation elements, with 8 and 5 cations occupying the A- and B-sites, respectively, using a laser-driven controllable synthesis technique. Such extreme mixing maximally promotes the formation of atomic-scale interfaces to markedly amplify atomic-level interfacial polarization loss for the effective dissipation of electromagnetic wave energy, thereby giving rise to a remarkably broad EAB of 9.3 GHz in high-entropy perovskites up to 600 degree celsius. Owing to the profoundly increased electrical conductivity that causes impedance mismatch at elevated temperatures, however, their EAB deteriorates to 4.6 GHz at 700 degree celsius. This work establishes a new extreme mixing design paradigm for enabling high-entropy oxide absorbers with unprecedented high-temperature EM absorption performance.

cond-mat.mtrl-sci↗

arXiv2026

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks

For peg-in-hole tasks, humans rely on binocular visual perception to locate the peg above the hole surface and then proceed with insertion. This paper draws insights from this behavior to enable agents to learn efficient assembly strategies through visual reinforcement learning. Hence, we propose a Separate Primitive Policy (S2P) to learn how to derive location and insertion actions simultaneously. S2P is compatible with model-free reinforcement learning algorithms. Ten insertion tasks featuring different polygons are developed as benchmarks for evaluations. Simulation experiments show that S2P can boost the sample efficiency and success rate even with force constraints. Real-world experiments are also performed to verify the feasibility of S2P. Ablations are finally given to discuss the generalizability of S2P and some factors that affect its performance.

cs.RO↗

arXiv2026

MorphOPC: Advancing Mask Optimization with Multi-scale Hierarchical Morphological Learning

As feature sizes shrink to the nanometer scale, accurately transferring circuit patterns from photomasks to silicon wafers becomes increasingly challenging. Optical proximity correction (OPC) is widely used to ensure pattern fidelity and manufacturability. Recent generative mask optimization models based on encoder-decoder architecture can synthesize near-optimal masks, serving as fast machine learning (ML) surrogates for traditional OPC. However, these models often fail to capture the geometric transformations from target layouts to mask patterns, leading to suboptimal quality. In this work, we formulate mask generation as a sequence of morphological operations on local layout features and propose \textit{MorphOPC}, a multi-scale hierarchical model with neural morphological modules to learn these transformations. Experiments on edge-based OPC and ILT benchmarks across metal and via layers show that \textit{MorphOPC} consistently outperforms state-of-the-art methods, achieving higher printing fidelity and lower manufacturing cost, demonstrating strong potential for scalable mask optimization.

cs.CV↗

arXiv2025

MaskOpt: A Large-Scale Mask Optimization Dataset to Advance AI in Integrated Circuit Manufacturing

As integrated circuit (IC) dimensions shrink below the lithographic wavelength, optical lithography faces growing challenges from diffraction and process variability. Model-based optical proximity correction (OPC) and inverse lithography technique (ILT) remain indispensable but computationally expensive, requiring repeated simulations that limit scalability. Although deep learning has been applied to mask optimization, existing datasets often rely on synthetic layouts, disregard standard-cell hierarchy, and neglect the surrounding contexts around the mask optimization targets, thereby constraining their applicability to practical mask optimization. To advance deep learning for cell- and context-aware mask optimization, we present MaskOpt, a large-scale benchmark dataset constructed from real IC designs at the 45$\mathrm{nm}$ node. MaskOpt includes 104,714 metal-layer tiles and 121,952 via-layer tiles. Each tile is clipped at a standard-cell placement to preserve cell information, exploiting repeated logic gate occurrences. Different context window sizes are supported in MaskOpt to capture the influence of neighboring shapes from optical proximity effects. We evaluate state-of-the-art deep learning models for IC mask optimization to build up benchmarks, and the evaluation results expose distinct trade-offs across baseline models. Further context size analysis and input ablation studies confirm the importance of both surrounding geometries and cell-aware inputs in achieving accurate mask generation.

cs.LG↗

arXiv2025

Laser-driven solid-state synthesis of high-entropy oxides

The vast compositional and structural landscape of high-entropy oxides (HEOs) grants them a wide range of potentially valuable physicochemical properties. However, the elemental immiscibility and crystal complexity limit their controllable synthesis. Here, we report a laser-driven solid-state synthesis technique that enables high-throughput production of HEOs with different crystal structures, including rock-salt, perovskite, spinel, fluorite, pyrochlore, tantalate, and silicate, incorporating up to 20 cationic elements. Typically, we successfully synthesize all types of high-entropy rare-earth disilicates (HEREDs), including A-, α-, \b{eta}-, γ-, δ-, F-, and G-type phase structures, with up to 15 rare-earth elements in the A site and 5 transition-metal elements in the B site. Benefiting from their unique G-type phase structure and 20-cation composition, HEREDs are endowed with the new functionality of microwave absorption (effective absorption bandwidth of 4.3 GHz). Our work not only realizes the controllable synthesis of HEOs with vast compositional and structural space but also offers them new physicochemical properties, making them highly promising for a diverse array of structural and functional applications.

cond-mat.mtrl-sci↗

arXiv2024

Machine learning potential-driven prediction of high-entropy ceramics with ultra-high melting points

Developing high-entropy ceramics (HECs) with ultra-high melting points (Tm) is crucial for their applications in ultra-high-temperature environments. However, related research has seldom been reported. Here, taking high-entropy diborides (HEBs) as an example, we develop a data-driven method to efficiently explore HEBs with ultra-high Tm via transferable machine-learning-potential-based molecular dynamics (MD). Specifically, a moment tensor potential (MTP) for HEBs with nine transition metal elements of group IVB, VB, and VIB is first constructed based on unary and binary diborides. Further studies on the performance of our constructed MTP have confirmed its remarkable accuracy, transferability, and reliability across both equimolar and non-equimolar HEB systems. Tm of HEBs are then accurately simulated through MD simulations based on the constructed MTP, and 24 features are simultaneously collected to enable reliable machine learning training. Five descriptors with the gradient boosting regression model are derived as the optimal combination for accurate Tm predictions in HEBs with genetic algorithms. Based on our established model, Tm of 32563 HEBs are eventually determined, achieving the maximum Tm of 3688 K in (Ti0.1Zr0.1Hf0.6Ta0.2)B2. The work presents a feasible approach to develop HECs with ultra-high Tm.

cond-mat.mtrl-sci↗

arXiv2024

Exploring mechanical and thermal properties of high-entropy ceramics via general machine learning potentials

The mechanical and thermal performance of high-entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high-entropy ceramic significantly hinders their development with desired mechanical and thermal properties. Herein, taking high-entropy carbides (HECs) as the model, we show the efficiency and effectiveness of exploring the mechanical and thermal properties via machine-learning-potential-based molecular dynamics (MD). Specifically, a general neuroevolution potential (NEP) with broad compositional applicability for HECs of ten transition metal elements from group IIIB-VIB is efficiently constructed from the small dataset comprising unary and binary carbides with an equal amount of ergodic chemical compositions. Based on this well-established NEP, MD simulations on mechanical and thermal properties of different HECs have shown good agreement with the results of first-principles calculations and experimental measurements, validating the accuracy, generalization, and reliability of using the developed general NEP in investigating mechanical and thermal performance of HECs. Our work provides an efficient solution to accelerate the search for high-entropy ceramics with desirable mechanical and thermal properties.

cond-mat.mtrl-sci↗

arXiv2024

Open-Source Reinforcement Learning Environments Implemented in MuJoCo with Franka Manipulator

This paper presents three open-source reinforcement learning environments developed on the MuJoCo physics engine with the Franka Emika Panda arm in MuJoCo Menagerie. Three representative tasks, push, slide, and pick-and-place, are implemented through the Gymnasium Robotics API, which inherits from the core of Gymnasium. Both the sparse binary and dense rewards are supported, and the observation space contains the keys of desired and achieved goals to follow the Multi-Goal Reinforcement Learning framework. Three different off-policy algorithms are used to validate the simulation attributes to ensure the fidelity of all tasks, and benchmark results are also given. Each environment and task are defined in a clean way, and the main parameters for modifying the environment are preserved to reflect the main difference. The repository, including all environments, is available at https://github.com/zichunxx/panda_mujoco_gym.

cs.RO↗

arXiv2024

Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making

Sampling-based motion planning (SBMP) algorithms are renowned for their robust global search capabilities. However, the inherent randomness in their sampling mechanisms often result in inconsistent path quality and limited search efficiency. In response to these challenges, this work proposes a novel deep learning-based motion planning framework, named Transformer-Enhanced Motion Planner (TEMP), which synergizes an Environmental Information Semantic Encoder (EISE) with a Motion Planning Transformer (MPT). EISE converts environmental data into semantic environmental information (SEI), providing MPT with an enriched environmental comprehension. MPT leverages an attention mechanism to dynamically recalibrate its focus on SEI, task objectives, and historical planning data, refining the sampling node generation. To demonstrate the capabilities of TEMP, we train our model using a dataset comprised of planning results produced by the RRT*. EISE and MPT are collaboratively trained, enabling EISE to autonomously learn and extract patterns from environmental data, thereby forming semantic representations that MPT could more effectively interpret and utilize for motion planning. Subsequently, we conducted a systematic evaluation of TEMP's efficacy across diverse task dimensions, which demonstrates that TEMP achieves exceptional performance metrics and a heightened degree of generalizability compared to state-of-the-art SBMPs.

cs.RO↗

arXiv2020

How many infections of COVID-19 there will be in the "Diamond Princess"-Predicted by a virus transmission model based on the simulation of crowd flow

Objectives: Simulate the transmission process of COVID-19 in a cruise ship, and then to judge how many infections there will be in the 3711 people in the "Diamond Princess" and analyze measures that could have prevented mass transmission. Methods: Based on the crowd flow model, the virus transmission rule between pedestrians is established, to simulate the spread of the virus caused by the close contact during pedestrians' daily activities on the cruise ship. Measurements and main results: Three types of simulation scenarios are designed, the Basic scenario focus on the process of virus transmission caused by a virus carrier and the effect of the personal protective measure against the virus. The condition that the original virus carriers had disembarked halfway and more and more people strengthen self-protection are considered in the Self-protection scenario, which would comparatively accord with the actual situation of "Diamond princess" cruise. Control scenario are set to simulate the effect of taking recommended or mandatory measures on virus transmission Conclusions: There are 850~1009 persons (with large probability) who have been infected with COVID-19 during the voyage of "Diamond Princess". The crowd infection percentage would be controlled effectively if the recommended or mandatory measures can be taken immediately during the alert phase of COVID-19 outbreaks.

physics.soc-ph↗