arXiv subjects

Xiaobai Ma

Publications and source records attributed to Xiaobai Ma.

11 recordsLinked to original sources

arXiv2026

Emergent Vortex Ordering in a Multiflavor Pyrochlore-Lattice Compound GeCo$_2$O$_4$

Entangled spin and orbital degrees of freedom provide a multiflavor route to novel magnetic states inaccessible in conventional spin systems. Here, we report the experimental identification of an emergent vortex lattice in the multiflavor pyrochlore-lattice compound GeCo$_2$O$_4$. By combining comprehensive neutron scattering experiments with a regularized regression framework, we identify substantial Kitaev interactions among the nearest-neighboring Co$^{2+}$ pseudospins, which cooperate with geometric frustration to stabilize the vortex order. These results reveal an unexpected route to vortex-lattice order in a three-dimensional Kitaev-frustrated magnet and demonstrate a regularized protocol for Hamiltonian determination in frustrated quantum materials.

cond-mat.str-el↗

arXiv2026

Giant anomalous Hall conductivity in frustrated magnet EuCo2Al9

The interaction between conduction electrons and localized magnetic moments profoundly influences the electrical and magnetic properties of materials, giving rise to a variety of fascinating physical phenomena and quantum effects. Here, we discover a giant anomalous Hall effect (AHE) in a frustrated Eu-based magnet, exhibiting a giant anomalous Hall conductivity (AHC) of 31000 Ω-1cm-1 and a remarkable anomalous Hall angle (AHA, tanθH) of 12 %--surpassing conventional mechanisms (either intrinsic or extrinsic) by two orders of magnitude. Combining magnetotransport, quantum oscillations, neutron diffraction and ab initio calculations, we establish that the giant AHC originates from fluctuating spin chirality skew scattering, generated by indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions of Eu-4f moments. Simultaneously, Hund's coupling of itinerant electrons and localized Eu-4f spins triggers giant exchange splitting, evidenced by temperature-dependent Fermi surface reconstruction. This work establishes a frustrated magnetic platform for engineering the AHE and elucidates the governing role of exchange interactions and spin textures in quantum transport, while also providing a framework for designing unconventional spintronic systems that harness emergent spin-texture dynamics.

cond-mat.str-el↗

arXiv2026

Directional selection of field-induced phases by weak anisotropy in triangular-lattice K$_2$Mn(SeO$_3$)$_2$

Triangular-lattice systems host a variety of ground states, ranging from quantum spin liquids to magnetically ordered phases, the latter of which can exhibit a sequence of magnetic phase transitions under applied magnetic fields. Here, we report magnetic and thermodynamic measurements, combined with powder and single-crystal neutron diffraction, on a high-spin, nearly isotropic Mn$^{2+}$ triangular-lattice system K$_2$Mn(SeO$_3$)$_2$. The compound undergoes long-range magnetic ordering below $T_\mathrm{N} \sim 4$~K in zero field. Contrary to expectations for an ideal Heisenberg system, the compound adopts an up-down-zero (UD0) magnetic structure down to the lowest temperature (0.05 K), rather than the commonly expected Y-type structure. This UD0 state is, however, highly sensitive to external magnetic fields. For fields applied along the $c$ axis, it is readily destabilized and replaced by the Y-type structure, followed by an up-up-down (UUD) phase corresponding to the 1/3 magnetization plateau. In contrast, when the field is applied within the triangular plane, the system evolves into a canted Y state at a higher critical field. These results reveal that weak anisotropy, though small in magnitude, exerts a strongly orientation-dependent influence, playing a key role in selecting the field-induced phases in this frustrated magnet.

cond-mat.str-el↗

arXiv2026

Magnetic field-induced non-trivial Lifshitz transition in TaCo2Te2

Magnetic-field-driven Lifshitz transitions are typically considered zero-temperature phenomena involving Fermi-surface reconstruction without symmetry breaking. Here, we report an unconventional Lifshitz transition in TaCo2Te2 that emerges exclusively within a narrow finite-temperature window under cooperative tuning by both temperature and magnetic field. Bulk-sensitive transport and thermoelectric measurements demonstrate continuous Fermi-surface renormalization at low temperatures, where the transition is sharply triggered by a critical magnetic field. Crucially, neutron diffraction reveals the absence of structural or magnetic phase transitions, while angle-resolved photoemission spectroscopy shows no spectral anomalies in electronic structure without magnetic field. These observations constrain the mechanism to a Zeeman-driven process invisible to equilibrium probes, establishing a paradigm where Fermi-surface topology is jointly controlled by temperature and magnetic field.

cond-mat.mtrl-sci↗

arXiv2025

Canted ferromagnetic order in a distorted triangular-lattice magnet Na$_2$SrCo(VO$_4$)$_2$

Triangular-lattice cobaltates with glaserite-type $X_2Y$Co($T$O$_4)_2$ structure provide an ideal platform to investigate intriguing quantum magnetism. Here we report a comprehensive study of the structural and magnetic properties of a triangular-lattice cobalt vanadate $\rm Na_2SrCo(VO_4)_2$. Room-temperature x-ray and neutron powder diffraction confirm that $\rm Na_2SrCo(VO_4)_2$ crystallizes in the monoclinic $P2_1/c$ space group with slightly distorted triangular layers of $\rm Co^{2+}$ ions. Magnetization measurements reveal a ferromagnetic transition at $T\rm_C \approx 3.4~{\rm K}$, where a sharp $λ$-type anomaly is observed in the specific heat. The magnetic entropy recovered up to 55 K approaches 90$\%$ of $R{\rm ln}2$, supporting an effective spin-1/2 state of Co$^{2+}$ ions at low temperature. Neutron diffraction at 2.3 K (below $T_{\rm C}$) further confirms a long-range canted ferromagnetic order with the Co$^{2+}$ moments aligned in the $ac$ plane and the ordered moment size of $\sim$ 2.6 $μ\rm_{B}$. Comparing with its sister compounds with a trigonal symmetry, $\rm Na_2BaCo(VO_4)_2$ with a collinear ferromagnetic structure and the recently discovered spin supersolid candidate $\rm Na_2BaCo(PO_4)_2$ with a distinct Y-like antiferromagnetic ground state, this study indicates the decisive role of the $T{\rm O_4}$ tetrahedra in tuning exchange interactions and contrasting magnetic behaviors of these glaserite-structure compounds.

cond-mat.str-el↗

arXiv2022

Recursive Reasoning Graph for Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requiring complex interactions, existing algorithms can suffer from an inability to accurately anticipate the influence of self-actions on other agents. Incorporating an ability to reason about other agents' potential responses can allow an agent to formulate more effective strategies. This paper adopts a recursive reasoning model in a centralized-training-decentralized-execution framework to help learning agents better cooperate with or compete against others. The proposed algorithm, referred to as the Recursive Reasoning Graph (R2G), shows state-of-the-art performance on multiple multi-agent particle and robotics games.

cs.LG↗

arXiv2021

Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships

Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios. However, identifying the subtle cues that can indicate drastically different outcomes remains an open problem with designing autonomous systems that operate in human environments. In this work, we show that explicitly inferring the latent state and encoding spatial-temporal relationships in a reinforcement learning framework can help address this difficulty. We encode prior knowledge on the latent states of other drivers through a framework that combines the reinforcement learner with a supervised learner. In addition, we model the influence passing between different vehicles through graph neural networks (GNNs). The proposed framework significantly improves performance in the context of navigating T-intersections compared with state-of-the-art baseline approaches.

cs.LG↗

arXiv2021

Extreme Suppression of Antiferromagnetic Order and Critical Scaling in a Two-Dimensional Random Quantum Magnet

Sr$_2$CuTeO$_6$ is a square-lattice Néel antiferromagnet with superexchange between first-neighbor $S=1/2$ Cu spins mediated by plaquette centered Te ions. Substituting Te by W, the affected impurity plaquettes have predominantly second-neighbor interactions, thus causing local magnetic frustration. Here we report a study of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$ using neutron diffraction and $μ$SR techniques, showing that the Néel order vanishes already at $x = 0.025 \pm 0.005$. We explain this extreme order suppression using a two-dimensional Heisenberg spin model, demonstrating that a W-type impurity induces a deformation of the order parameter that decays with distance as $1/r^2$ at temperature $T=0$. The associated logarithmic singularity leads to loss of order for any $x>0$. Order for small $x>0$ and $T>0$ is induced by weak interplane couplings. In the nonmagnetic phase of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$, the $μ$SR relaxation rate exhibits quantum critical scaling with a large dynamic exponent, $z \approx 3$, consistent with a random-singlet state.

cond-mat.str-el↗

arXiv2020

Handling Missing Data with Graph Representation Learning

Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels are learned directly from incomplete data. However, existing imputation models tend to have strong prior assumptions and cannot learn from downstream tasks, while models targeting label prediction often involve heuristics and can encounter scalability issues. Here we propose GRAPE, a graph-based framework for feature imputation as well as label prediction. GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges. Under the GRAPE framework, the feature imputation is formulated as an edge-level prediction task and the label prediction as a node-level prediction task. These tasks are then solved with Graph Neural Networks. Experimental results on nine benchmark datasets show that GRAPE yields 20% lower mean absolute error for imputation tasks and 10% lower for label prediction tasks, compared with existing state-of-the-art methods.

cs.LG↗

arXiv2019

Monte-Carlo Tree Search for Policy Optimization

Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free methods (e.g., genetic algorithms or evolution strategies) help mitigate these issues, poor initialization and local optima are still concerns in highly nonconvex spaces. This paper presents a method for policy optimization based on Monte-Carlo tree search and gradient-free optimization. Our method, called Monte-Carlo tree search for policy optimization (MCTSPO), provides a better exploration-exploitation trade-off through the use of the upper confidence bound heuristic. We demonstrate improved performance on reinforcement learning tasks with deceptive or sparse reward functions compared to popular gradient-based and deep genetic algorithm baselines.

cs.LG↗

arXiv2019

Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system and disturbances. This paper examines two different algorithms to solve the game, Robust Adversarial Reinforcement Learning and Neural Fictitious Self Play, and compares performance on an autonomous driving scenario. We extend the game formulation to a semi-competitive setting and demonstrate that the resulting adversary better captures meaningful disturbances that lead to better overall performance. The resulting robust policy exhibits improved driving efficiency while effectively reducing collision rates compared to baseline control policies produced by traditional reinforcement learning methods.

cs.LG↗