arXiv subjects

Zhenhao Zhou

Publications and source records attributed to Zhenhao Zhou.

13 recordsLinked to original sources

arXiv2026

Autonomous Optimization of Complex Oxides for Thermochemical Fuel Production

Two-step thermochemical fuel production, including H2O and CO2 splitting, offers a promising route to sustainable fuel manufacturing, with performance governed by redox-active oxides that enable cyclic reduction-oxidation reactions. Maximizing thermal-to-fuel conversion efficiency demands materials that simultaneously satisfy multiple stringent thermodynamic and kinetic targets. Addressing these requirements has increasingly driven materials design toward complex, multi-cation oxides, such as mixed-cation fluorites, perovskites, and high-entropy oxides, wherein composition, defect chemistry, phase stability, and morphology should be co-optimized. This creates a challenging materials optimization problem that is poorly suited to traditional trial-and-error approaches. In this review, we argue that thermochemical fuel production provides a compelling frontier for autonomous materials design and optimization. We first examine why redox-active complex oxides are difficult to develop, owing to multidimensional phase spaces, harsh operating conditions, and competing functional targets. We then discuss how high-throughput computation, automated synthesis, characterization and testing, and machine learning can be integrated into closed-loop workflows to address these challenges. Building on broader oxide materials research, we organize recent progress into a capability roadmap for complex-oxide optimization, spanning compositionally diverse synthesis, operando characterization, robotic testing, operation-condition computation, and multi-objective optimization. Finally, we outline key experimental, computational, and data challenges for building self-improving materials development platforms for materials development in thermochemical fuel production.

cond-mat.mtrl-sci↗

arXiv2026

Mechanism-Dependent Descriptors Enable Predictive Design of Oxygen Capacity in Perovskite Oxides

Perovskite oxides can reversibly accommodate substantial changes in oxygen stoichiometry, making them attractive for clean-energy technologies including chemical looping and oxygen storage. Despite extensive efforts to optimize their redox properties, predictive descriptors capable of assessing oxygen capacity across diverse compositions remain under development. Here, we combine experiments and first-principles calculations to establish composition and oxygen-capacity relationships in the model perovskite series LnxSr1-xCoO3. We confirm that increasing Sr2+ content promotes the formation of high-valence Co4+, expanding the cationic redox reservoir available during oxygen release and thereby enhancing oxygen capacity. In this regime, oxygen-vacancy formation energy captures the observed trend because oxygen release is primarily compensated by Co4+/Co3+/Co2+ redox. Across the rare-earth series, however, oxygen capacity decreases from La to Lu despite progressively lower oxygen-vacancy formation energies. We reveal that this counterintuitive behavior originates from an alternative charge-compensation pathway, in which lattice oxygen is partially oxidized to O1- -like species during oxygen removal. Heavy rare-earth compositions (Tb-Lu) preferentially stabilize these oxygen-hole species through distinct local bonding environments, with charge compensation involving both oxidized lattice oxygen and reduced rare-earth and cobalt cations, thereby suppressing net oxygen release despite favorable vacancy thermodynamics. We further identify average metal-oxygen bond strength, quantified by integrated crystal orbital Hamilton population, as a physically meaningful descriptor for oxygen capacity when anionic redox becomes dominant.

cond-mat.mtrl-sci↗

arXiv2026

Building a physics-aware AI ecosystem for solid-state hydrogen storage materials

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By embedding physical constraints and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.

cond-mat.mtrl-sci↗

arXiv2026

A Total Lagrangian Finite Element Framework for Multibody Dynamics: Part II -- GPU Implementation and Numerical Experiments

We present the numerical methods and GPU-accelerated implementation underlying a Total Lagrangian finite element framework for finite-deformation flexible multibody dynamics, introduced in the companion paper [1]. The framework supports 10-node quadratic tetrahedral (T10) elements and ANCF beam and shell elements, with quadrature-based hyperelastic response (St. Venant-Kirchhoff and Mooney-Rivlin) and an optional Kelvin-Voigt viscous stress contribution. Time stepping employs a velocity-based implicit backward-Euler scheme, yielding a nonlinear residual in velocity that couples inertia, internal and external forces, and bilateral constraints. Constraints are enforced via an augmented Lagrangian method (ALM), structured as an outer loop alternating an inner velocity solve with a dual-ascent multiplier update. We introduce a two-stage GPU parallelization strategy for internal force and tangent stiffness evaluation, and provide two inner solvers: a first-order AdamW optimizer and a second-order Newton solver that assembles and factorizes a sparse global Hessian on the GPU using cuDSS. A fixed-sparsity matrix strategy eliminates repeated symbolic analysis and enables efficient numerical refactorization across Newton iterations. For collision detection, we present a GPU-native two-thread asynchronous algorithm operating on triangle soups, avoiding bounding-volume hierarchies entirely. Systematic scaling benchmarks across all three supported element types and six mesh resolutions show that the Newton solver achieves approximately one order of magnitude reduction in real-time factor relative to CPU baselines at the largest resolutions tested. The frictional contact model is validated against closed-form rigid-body predictions through quasi-static and dynamic impact unit tests.

cs.CE↗

arXiv2026

Does Pass Rate Tell the Whole Story? Evaluating Design Constraint Compliance in LLM-based Issue Resolution

Repository-level issue resolution benchmarks have become a standard testbed for evaluating LLM-based agents, yet success is still predominantly measured by test pass rates. In practice, however, acceptable patches must also comply with project-specific design constraints, such as architectural conventions, error-handling policies, and maintainability requirements, which are rarely encoded in tests and are often documented only implicitly in code review discussions. This paper introduces \textit{design-aware issue resolution} and presents \bench{}, a benchmark that makes such implicit design constraints explicit and measurable. \bench{} is constructed by mining and validating design constraints from real-world pull requests, linking them to issue instances, and automatically checking patch compliance using an LLM-based verifier, yielding 495 issues and 1,787 validated constraints across six repositories, aligned with SWE-bench-Verified and SWE-bench-Pro. Experiments with state-of-the-art agents show that test-based correctness substantially overestimates patch quality: fewer than half of resolved issues are fully design-satisfying, design violations are widespread, and functional correctness exhibits negligible statistical association with design satisfaction. While providing issue-specific design guidance reduces violations, substantial non-compliance remains, highlighting a fundamental gap in current agent capabilities and motivating design-aware evaluation beyond functional correctness.

cs.SE↗

arXiv2026

A Total Lagrangian Finite Element Framework for Multibody Dynamics: Part I -- Formulation

We present a Total Lagrangian finite element framework for finite-deformation multibody dynamics. The framework combines a compact kinematic representation, a deformation-gradient-based formulation, an element-agnostic constitutive interface, and a systematic constraint-construction machinery for coupling deformable bodies through engineering joints. Within this setting, we derive the equations of motion for collections of deformable bodies and formulate their response in the presence of external loads, frictional contact forces, and constraint reaction forces. The framework accommodates field forces applied pointwise, over surfaces, or throughout volumes, and supports material models of practical interest, including Mooney-Rivlin, Neo-Hookean, and Kelvin-Voigt. A companion paper discusses the GPU-accelerated implementation of the framework outlined herein and reports on numerical experiments and benchmark results.

cs.CE↗

arXiv2025

ChronoDreamer: Action-Conditioned World Model as an Online Simulator for Robotic Planning

We present ChronoDreamer, an action-conditioned world model for contact-rich robotic manipulation. Given a history of egocentric RGB frames, contact maps, actions, and joint states, ChronoDreamer predicts future video frames, contact distributions, and joint angles via a spatial-temporal transformer trained with MaskGIT-style masked prediction. Contact is encoded as depth-weighted Gaussian splat images that render 3D forces into a camera-aligned format suitable for vision backbones. At inference, predicted rollouts are evaluated by a vision-language model that reasons about collision likelihood, enabling rejection sampling of unsafe actions before execution. We train and evaluate on DreamerBench, a simulation dataset generated with Project Chrono that provides synchronized RGB, contact splat, proprioception, and physics annotations across rigid and deformable object scenarios. Qualitative results demonstrate that the model preserves spatial coherence during non-contact motion and generates plausible contact predictions, while the LLM-based judge distinguishes collision from non-collision trajectories.

cs.AI↗

arXiv2025

Extracting Conceptual Knowledge to Locate Software Issues

Issue localization, which identifies faulty code elements such as files or functions, is critical for effective bug fixing. While recent LLM-based and LLM-agent-based approaches improve accuracy, they struggle in large-scale repositories due to concern tangling, where relevant logic is buried in large functions, and concern scattering, where related logic is dispersed across files. To address these challenges, we propose RepoLens, a novel approach that abstracts and leverages conceptual knowledge from code repositories. RepoLens decomposes fine-grained functionalities and recomposes them into high-level concerns, semantically coherent clusters of functionalities that guide LLMs. It operates in two stages: an offline stage that extracts and enriches conceptual knowledge into a repository-wide knowledge base, and an online stage that retrieves issue-specific terms, clusters and ranks concerns by relevance, and integrates them into localization workflows via minimally intrusive prompt enhancements. We evaluate RepoLens on SWE-Lancer-Loc, a benchmark of 216 tasks derived from SWE-Lancer. RepoLens consistently improves three state-of-the-art tools, namely AgentLess, OpenHands, and mini-SWE-agent, achieving average gains of over 22% in Hit@k and 46% in Recall@k for file- and function-level localization. It generalizes across models (GPT-4o, GPT-4o-mini, GPT-4.1) with Hit@1 and Recall@10 gains up to 504% and 376%, respectively. Ablation studies and manual evaluation confirm the effectiveness and reliability of the constructed concerns.

cs.SE↗

arXiv2025

Interpretable Nanoporous Materials Design with Symmetry-Aware Networks

Reticular frameworks hold promise for diverse sustainable applications, yet their immense chemical space limits efficient and systematic design. While machine learning provides a compelling pathway to accelerate exploration, existing approaches often lack either interpretability or fidelity in linking crystal geometry to emergent properties. Here, we introduce a site-resolved equivariant learning framework based on three-dimensional periodic space sampling, which decomposes reticular structures into local geometric environments for simultaneous property prediction and site-wise contribution analysis. Trained on a combination of constructed and retrieved datasets, the model achieves state-of-the-art accuracy and data efficiency across gas storage, gas separation, and electronic-property prediction tasks. Importantly, the framework reveals interpretable local structure-property relationships by identifying transferable high-contribution sites across diverse frameworks. Leveraging these learned motifs, we further demonstrate inverse design of new metal-organic frameworks exhibiting record-high N2 storage, strong CO2/N2 separation performance, and near-zero electronic band gaps, validated by physics-based simulations.

cond-mat.mtrl-sci↗

arXiv2025

Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults

The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affecting billions of users. Fault localization (FL), which aims at identifying the buggy code elements in software, plays an essential role in software quality assurance. While recent LLM agents have achieved promising accuracy in FL on recent benchmarks like SWE-bench, it remains unclear how well these methods perform in the Linux kernel, where FL is much more challenging due to the large-scale code base, limited observability, and diverse impact factors. In this paper, we introduce LinuxFLBench, a FL benchmark constructed from real-world Linux kernel bugs. We conduct an empirical study to assess the performance of state-of-the-art LLM agents on the Linux kernel. Our initial results reveal that existing agents struggle with this task, achieving a best top-1 accuracy of only 41.6% at file level. To address this challenge, we propose LinuxFL$^+$, an enhancement framework designed to improve FL effectiveness of LLM agents for the Linux kernel. LinuxFL$^+$ substantially improves the FL accuracy of all studied agents (e.g., 7.2% - 11.2% accuracy increase) with minimal costs.

cs.AI↗

arXiv2024

Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention

This paper develops a novel car-following control method to reduce voluntary driver interventions and improve traffic stability in Automated Vehicles (AVs). Through a combination of experimental and empirical analysis, we show how voluntary driver interventions can instigate substantial traffic disturbances that are amplified along the traffic upstream. Motivated by these findings, we present a framework for driver intervention based on evidence accumulation (EA), which describes the evolution of the driver's distrust in automation, ultimately resulting in intervention. Informed through the EA framework, we propose a deep reinforcement learning (DRL)-based car-following control for AVs that is strategically designed to mitigate unnecessary driver intervention and improve traffic stability. Numerical experiments are conducted to demonstrate the effectiveness of the proposed control model.

cs.HC↗

arXiv2024

A Study on the Use of Simulation in Synthesizing Path-Following Control Policies for Autonomous Ground Robots

We report results obtained and insights gained while answering the following question: how effective is it to use a simulator to establish path following control policies for an autonomous ground robot? While the quality of the simulator conditions the answer to this question, we found that for the simulation platform used herein, producing four control policies for path planning was straightforward once a digital twin of the controlled robot was available. The control policies established in simulation and subsequently demonstrated in the real world are PID control, MPC, and two neural network (NN) based controllers. Training the two NN controllers via imitation learning was accomplished expeditiously using seven simple maneuvers: follow three circles clockwise, follow the same circles counter-clockwise, and drive straight. A test randomization process that employs random micro-simulations is used to rank the ``goodness'' of the four control policies. The policy ranking noted in simulation correlates well with the ranking observed when the control policies were tested in the real world. The simulation platform used is publicly available and BSD3-released as open source; a public Docker image is available for reproducibility studies. It contains a dynamics engine, a sensor simulator, a ROS2 bridge, and a ROS2 autonomy stack the latter employed both in the simulator and the real world experiments.

cs.RO↗

arXiv2024

Quantifying the Sim2real Gap for GPS and IMU Sensors

Simulation can and should play a critical role in the development and testing of algorithms for autonomous agents. What might reduce its impact is the ``sim2real'' gap -- the algorithm response differs between operation in simulated versus real-world environments. This paper introduces an approach to evaluate this gap, focusing on the accuracy of sensor simulation -- specifically IMU and GPS -- in velocity estimation tasks for autonomous agents. Using a scaled autonomous vehicle, we conduct 40 real-world experiments across diverse environments then replicate the experiments in simulation with five distinct sensor noise models. We note that direct comparison of raw simulation and real sensor data fails to quantify the sim2real gap for robotics applications. We demonstrate that by using a state of the art state-estimation package as a ``judge'', and by evaluating the performance of this state-estimator in both real and simulated scenarios, we can isolate the sim2real discrepancies stemming from sensor simulations alone. The dataset generated is open-source and publicly available for unfettered use.

cs.RO↗