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Lijun Wu

Lijun Wu appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.mtrl-sciArtificial IntelligenceComputation and Languagecond-mat.mes-hallMachine LearningComputer Visioncond-mat.str-elBiomoleculescond-mat.supr-conphysics.opticscond-mat.softInformation Retrievalphysics.chem-phQuantitative Methods

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Published work

42 published item(s)

preprint2026arXiv

GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models

The advent of Unified Multimodal Models (UMMs) signals a paradigm shift in artificial intelligence, moving from passive perception to active, cross-modal generation. Despite their unprecedented ability to synthesize information, a critical gap persists in evaluation: existing benchmarks primarily assess discriminative understanding or unconstrained image generation separately, failing to measure the integrated cognitive process of generative reasoning. To bridge this gap, we propose that geometric construction provides an ideal testbed as it inherently demands a fusion of language comprehension and precise visual generation. We introduce GGBench, a benchmark designed specifically to evaluate geometric generative reasoning. It provides a comprehensive framework for systematically diagnosing a model's ability to not only understand and reason but to actively construct a solution, thereby setting a more rigorous standard for the next generation of intelligent systems. Project website: https://opendatalab-raiser.github.io/GGBench/.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

GTR-CoT: Graph Traversal as Visual Chain of Thought for Molecular Structure Recognition

Optical Chemical Structure Recognition (OCSR) is essential for converting molecular images into machine-readable formats. While recent vision-language models (VLMs) have shown promise, their image-captioning approach often struggles with complex molecular structures and inconsistent annotations. To address these issues, we introduce GTR-VL, featuring two key innovations: (1) the \textit{Graph Traversal as Visual Chain of Thought} mechanism that emulates human reasoning by incrementally parsing molecular graphs through sequential atom-bond predictions, and (2) the data-centric \textit{Faithfully Recognize What You've Seen} principle, which aligns abbreviated structures in images with their expanded annotations. For hand-drawn OCSR tasks, where datasets lack graph annotations and only provide final SMILES, we apply reinforcement learning using the GRPO method, introducing reward mechanisms like format reward, graph reward, and SMILES reward. This approach significantly enhances performance in hand-drawn recognition tasks through weak supervision. We developed GTR-1.3M, a large-scale instruction-tuning dataset with corrected annotations, and MolRec-Bench, the first benchmark for fine-grained evaluation of graph-parsing accuracy in OCSR. Our two-stage training scheme involves SFT training for printed images and the GRPO method for transferring capabilities to hand-drawn tasks. Experiments show that GTR-VL outperforms specialist models, chemistry-domain VLMs, and commercial VLMs on both printed and hand-drawn datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

LRAS: Advanced Legal Reasoning with Agentic Search

While Large Reasoning Models (LRMs) have demonstrated exceptional logical capabilities in mathematical domains, their application to the legal field remains hindered by the strict requirements for procedural rigor and adherence to legal logic. Existing legal LLMs, which rely on "closed-loop reasoning" derived solely from internal parametric knowledge, frequently suffer from lack of self-awareness regarding their knowledge boundaries, leading to confident yet incorrect conclusions. To address this challenge, we present Legal Reasoning with Agentic Search (LRAS), the first framework designed to transition legal LLMs from static and parametric "closed-loop thinking" to dynamic and interactive "Active Inquiry". By integrating Introspective Imitation Learning and Difficulty-aware Reinforcement Learning, LRAS enables LRMs to identify knowledge boundaries and handle legal reasoning complexity. Empirical results demonstrate that LRAS outperforms state-of-the-art baselines by 8.2-32\%, with the most substantial gains observed in tasks requiring deep reasoning with reliable knowledge. We will release our data and models for further exploration soon.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MolRecBench-Wild: A Real-World Benchmark for Optical Chemical Structure Recognition

Optical Chemical Structure Recognition (OCSR) aims to translate molecular diagrams in scientific literature into machine-readable formats, but current systems remain unreliable on real-world images due to substantial visual and chemical complexity. We introduce MOSAIC, a dual-dimensional difficulty framework with 37 fine-grained labels that jointly characterize visual interference and chemical semantic challenges in molecular diagrams. Based on this framework, we construct MolRecBench-Wild, a benchmark of 5,029 structures from 820 recent chemistry papers, covering the full difficulty spectrum observed in real publications. To enable faithful semantic evaluation beyond SMILES and MolFile, we propose CARBON, a representation language capable of expressing valence variations, icon-based groups, and other non-standard chemical semantics. We further adopt a dual-track evaluation protocol supporting both CARBON and SMILES outputs for broad model compatibility. Comprehensive experiments over 18 OCSR-capable models reveal severe performance degradation on MolRecBench-Wild, exposing a large gap between previous patent benchmarks and real-world academic scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PM4Bench: Benchmarking Large Vision-Language Models with Parallel Multilingual Multi-Modal Multi-task Corpus

While Large Vision-Language Models (LVLMs) demonstrate promising multilingual capabilities, their evaluation is currently hindered by two critical limitations: (1) the use of non-parallel corpora, which conflates inherent language capability gaps with dataset artifacts, precluding a fair assessment of cross-lingual alignment; and (2) disjointed multimodal inputs, which deviate from real-world scenarios where most texts are embedded within visual contexts. To address these challenges, we propose PM4Bench, the first Multilingual Multi-Modal Multi-task Benchmark constructed on a strictly parallel corpus across 10 languages. By eliminating content divergence, our benchmark enables a fair comparison of model capabilities across different languages. We also introduce a vision setting where textual queries are visually fused into images, compelling models to jointly "see," "read," and "think". Extensive evaluation of 10 LVLMs uncover a substantial performance drop in the Vision setting compared to standard inputs. Further analysis reveals that OCR capability is not only a general bottleneck but also contributes to cross-lingual performance disparities, suggesting that improving multilingual OCR is essential for advancing LVLM performance. We will release PM4Bench at https://github.com/opendatalab/PM4Bench .

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning

Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision Language Models (LVLMs) handle naturally. We introduce a strategy termed BBox and Index as Visual Prompt (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the RxnCaption-15k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry. We will release data, models, and code on GitHub.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SpatiaLoc: Leveraging Multi-Level Spatial Enhanced Descriptors for Cross-Modal Localization

Cross-modal localization using text and point clouds enables robots to localize themselves via natural language descriptions, with applications in autonomous navigation and interaction between humans and robots. In this task, objects often recur across text and point clouds, making spatial relationships the most discriminative cues for localization. Given this characteristic, we present SpatiaLoc, a framework utilizing a coarse-to-fine strategy that emphasizes spatial relationships at both the instance and global levels. In the coarse stage, we introduce a Bezier Enhanced Object Spatial Encoder (BEOSE) that models spatial relationships at the instance level using quadratic Bezier curves. Additionally, a Frequency Aware Encoder (FAE) generates spatial representations in the frequency domain at the global level. In the fine stage, an Uncertainty Aware Gaussian Fine Localizer (UGFL) regresses 2D positions by modeling predictions as Gaussian distributions with a loss function aware of uncertainty. Extensive experiments on KITTI360Pose demonstrate that SpatiaLoc significantly outperforms existing state-of-the-art (SOTA) methods.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets

The construction of Supervised Fine-Tuning (SFT) datasets is a critical yet under-theorized stage in the post-training of Large Language Models (LLMs), as prevalent practices often rely on heuristic aggregation without a systematic understanding of how individual samples contribute to model performance. In this report, we propose a paradigm shift from ad-hoc curation to a closed-loop dataset engineering framework using OpenDataArena (ODA), which leverages value-anchored rankings and multi-dimensional analysis to transform value benchmarking into feedback signals guiding dataset construction. We instantiate this methodology through two new datasets: \textbf{ODA-Math-460k}, a specialized mathematics reasoning dataset that utilizes a novel two-stage difficulty-aware pipeline to achieve State-of-the-Art (SOTA) results on benchmarks such as AIME and HMMT, and \textbf{ODA-Mixture (100k \& 500k)}, a series of multi-domain instruction datasets built via an ``Anchor-and-Patch'' strategy that outperforms significantly larger open-source baselines. Our empirical results demonstrate that ODA-driven datasets significantly improve both domain-specific reasoning and general utility while achieving superior data efficiency, validating a transition toward data-centric AI where transparent evaluation serves as the primary engine for engineering high-quality training data.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

FABind: Fast and Accurate Protein-Ligand Binding

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose $\mathbf{FABind}$, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. $\mathbf{FABind}$ incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed $\mathbf{FABind}$ demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at https://github.com/QizhiPei/FABind

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Mutually Reinforced Framework for Pretrained Sentence Embeddings

The lack of labeled data is a major obstacle to learning high-quality sentence embeddings. Recently, self-supervised contrastive learning (SCL) is regarded as a promising way to address this problem. However, the existing works mainly rely on hand-crafted data annotation heuristics to generate positive training samples, which not only call for domain expertise and laborious tuning, but are also prone to the following unfavorable cases: 1) trivial positives, 2) coarse-grained positives, and 3) false positives. As a result, the self-supervision's quality can be severely limited in reality. In this work, we propose a novel framework InfoCSE to address the above problems. Instead of relying on annotation heuristics defined by humans, it leverages the sentence representation model itself and realizes the following iterative self-supervision process: on one hand, the improvement of sentence representation may contribute to the quality of data annotation; on the other hand, more effective data annotation helps to generate high-quality positive samples, which will further improve the current sentence representation model. In other words, the representation learning and data annotation become mutually reinforced, where a strong self-supervision effect can be derived. Extensive experiments are performed based on three benchmark datasets, where notable improvements can be achieved against the existing SCL-based methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Direct Molecular Conformation Generation

Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previous methods usually first predict the interatomic distances, the gradients of interatomic distances or the local structures (e.g., torsion angles) of a molecule, and then reconstruct its 3D conformation. How to directly generate the conformation without the above intermediate values is not fully explored. In this work, we propose a method that directly predicts the coordinates of atoms: (1) the loss function is invariant to roto-translation of coordinates and permutation of symmetric atoms; (2) the newly proposed model adaptively aggregates the bond and atom information and iteratively refines the coordinates of the generated conformation. Our method achieves the best results on GEOM-QM9 and GEOM-Drugs datasets. Further analysis shows that our generated conformations have closer properties (e.g., HOMO-LUMO gap) with the groundtruth conformations. In addition, our method improves molecular docking by providing better initial conformations. All the results demonstrate the effectiveness of our method and the great potential of the direct approach. The code is released at https://github.com/DirectMolecularConfGen/DMCG

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RetroGraph: Retrosynthetic Planning with Graph Search

Retrosynthetic planning, which aims to find a reaction pathway to synthesize a target molecule, plays an important role in chemistry and drug discovery. This task is usually modeled as a search problem. Recently, data-driven methods have attracted many research interests and shown promising results for retrosynthetic planning. We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e.g., AND-OR tree search, Monte Carlo tree search). Such redundancies make the search process inefficient. We propose a graph-based search policy that eliminates the redundant explorations of any intermediate molecules. As searching over a graph is more complicated than over a tree, we further adopt a graph neural network to guide the search over graphs. Meanwhile, our method can search a batch of targets together in the graph and remove the inter-target duplication in the tree-based search methods. Experimental results on two datasets demonstrate the effectiveness of our method. Especially on the widely used USPTO benchmark, we improve the search success rate to 99.47%, advancing previous state-of-the-art performance for 2.6 points.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design

Structure-based drug design is drawing growing attentions in computer-aided drug discovery. Compared with the virtual screening approach where a pre-defined library of compounds are computationally screened, de novo drug design based on the structure of a target protein can provide novel drug candidates. In this paper, we present a generative solution named TamGent (Target-aware molecule generator with Transformer) that can directly generate candidate drugs from scratch for a given target, overcoming the limits imposed by existing compound libraries. Following the Transformer framework (a state-of-the-art framework in deep learning), we design a variant of Transformer encoder to process 3D geometric information of targets and pre-train the Transformer decoder on 10 million compounds from PubChem for candidate drug generation. Systematical evaluation on candidate compounds generated for targets from DrugBank shows that both binding affinity and drugability are largely improved. TamGent outperforms previous baselines in terms of both effectiveness and efficiency. The method is further verified by generating candidate compounds for the SARS-CoV-2 main protease and the oncogenic mutant KRAS G12C. The results show that our method not only re-discovers previously verified drug molecules , but also generates novel molecules with better docking scores, expanding the compound pool and potentially leading to the discovery of novel drugs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Unified 2D and 3D Pre-Training of Molecular Representations

Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represented by a 3D conformation with 3-dimensional coordinates of all atoms. We note that most previous work handles 2D and 3D information separately, while jointly leveraging these two sources may foster a more informative representation. In this work, we explore this appealing idea and propose a new representation learning method based on a unified 2D and 3D pre-training. Atom coordinates and interatomic distances are encoded and then fused with atomic representations through graph neural networks. The model is pre-trained on three tasks: reconstruction of masked atoms and coordinates, 3D conformation generation conditioned on 2D graph, and 2D graph generation conditioned on 3D conformation. We evaluate our method on 11 downstream molecular property prediction tasks: 7 with 2D information only and 4 with both 2D and 3D information. Our method achieves state-of-the-art results on 10 tasks, and the average improvement on 2D-only tasks is 8.3%. Our method also achieves significant improvement on two 3D conformation generation tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

IOT: Instance-wise Layer Reordering for Transformer Structures

With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and many variants have been proposed. Currently, almost all these models assume that the layer order is fixed and kept the same across data samples. We observe that different data samples actually favor different orders of the layers. Based on this observation, in this work, we break the assumption of the fixed layer order in the Transformer and introduce instance-wise layer reordering into the model structure. Our Instance-wise Ordered Transformer (IOT) can model variant functions by reordered layers, which enables each sample to select the better one to improve the model performance under the constraint of almost the same number of parameters. To achieve this, we introduce a light predictor with negligible parameter and inference cost to decide the most capable and favorable layer order for any input sequence. Experiments on 3 tasks (neural machine translation, abstractive summarization, and code generation) and 9 datasets demonstrate consistent improvements of our method. We further show that our method can also be applied to other architectures beyond Transformer. Our code is released at Github.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Learning to Reweight with Deep Interactions

Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training data using a teacher model, receives much attention due to its simplicity and effectiveness. In existing learning to reweight works, the teacher model only utilizes shallow/surface information such as training iteration number and loss/accuracy of the student model from training/validation sets, but ignores the internal states of the student model, which limits the potential of learning to reweight. In this work, we propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model, and the teacher model returns adaptive weights of training samples to enhance the training of the student model. The teacher model is jointly trained with the student model using meta gradients propagated from a validation set. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Concurrent probing of electron-lattice dephasing induced by photoexcitation in 1T-TaSeTe using ultrafast electron diffraction

It has been technically challenging to concurrently probe the electrons and the lattices in materials during non-equilibrium processes, allowing their correlations to be determined. Here, in a single set of ultrafast electron diffraction patterns taken on the charge-density-wave (CDW) material 1T-TaSeTe, we discover a temporal shift in the diffraction intensity measurements as a function of scattering angle. With the help of dynamic models and theoretical calculations, we show that the ultrafast electrons probe both the valence-electron and lattice dynamic processes, resulting in the temporal shift measurements. Our results demonstrate unambiguously that the CDW is not merely a result of the periodic lattice deformation ever-present in 1T-TaSeTe but has significant electronic origin. This method demonstrates a novel approach for studying many quantum effects that arise from electron-lattice dephasing in molecules and crystals for next-generation devices.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

One-stage detector basically formulates object detection as dense classification and localization. The classification is usually optimized by Focal Loss and the box location is commonly learned under Dirac delta distribution. A recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization, where the predicted quality facilitates the classification to improve detection performance. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Two problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference and (2) the inflexible Dirac delta distribution for localization when there is ambiguity and uncertainty in complex scenes. To address the problems, we design new representations for these elements. Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations. The improved representations eliminate the inconsistency risk and accurately depict the flexible distribution in real data, but contain continuous labels, which is beyond the scope of Focal Loss. We then propose Generalized Focal Loss (GFL) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. On COCO test-dev, GFL achieves 45.0\% AP using ResNet-101 backbone, surpassing state-of-the-art SAPD (43.5\%) and ATSS (43.6\%) with higher or comparable inference speed, under the same backbone and training settings. Notably, our best model can achieve a single-model single-scale AP of 48.2\%, at 10 FPS on a single 2080Ti GPU. Code and models are available at https://github.com/implus/GFocal.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Incorporating BERT into Neural Machine Translation

The recently proposed BERT has shown great power on a variety of natural language understanding tasks, such as text classification, reading comprehension, etc. However, how to effectively apply BERT to neural machine translation (NMT) lacks enough exploration. While BERT is more commonly used as fine-tuning instead of contextual embedding for downstream language understanding tasks, in NMT, our preliminary exploration of using BERT as contextual embedding is better than using for fine-tuning. This motivates us to think how to better leverage BERT for NMT along this direction. We propose a new algorithm named BERT-fused model, in which we first use BERT to extract representations for an input sequence, and then the representations are fused with each layer of the encoder and decoder of the NMT model through attention mechanisms. We conduct experiments on supervised (including sentence-level and document-level translations), semi-supervised and unsupervised machine translation, and achieve state-of-the-art results on seven benchmark datasets. Our code is available at \url{https://github.com/bert-nmt/bert-nmt}.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multi-branch Attentive Transformer

While the multi-branch architecture is one of the key ingredients to the success of computer vision tasks, it has not been well investigated in natural language processing, especially sequence learning tasks. In this work, we propose a simple yet effective variant of Transformer called multi-branch attentive Transformer (briefly, MAT), where the attention layer is the average of multiple branches and each branch is an independent multi-head attention layer. We leverage two training techniques to regularize the training: drop-branch, which randomly drops individual branches during training, and proximal initialization, which uses a pre-trained Transformer model to initialize multiple branches. Experiments on machine translation, code generation and natural language understanding demonstrate that such a simple variant of Transformer brings significant improvements. Our code is available at \url{https://github.com/HA-Transformer}.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Vacancy defect control of colossal thermopower in FeSb2

Iron diantimonide is a material with the highest known thermoelectric power. By combining scanning transmission electron microscope study with electronic transport neutron, X-ray scattering and first principle calculation we identify atomic defects that control colossal thermopower magnitude and nanoprecipitate clusters with Sb vacancy ordering which induce additional phonon scattering and substantially reduce thermal conductivity. Defects are found to cause rather weak but important monoclinic distortion of the unit cell Pnnm to Pm. The absence of Sb along [010] for high defect concentration forms conducting path due to Fe d orbital overlap. The connection between atomic defect anisotropy and colossal thermopower in FeSb2 paves the way for the understanding and tailoring of giant thermopower in related materials.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Water-induced formation of an alkali-ion dimer in cryptomelane nanorods

Tunneled metal oxides such as $α-$Mn$_8$O$_{16}$ (hollandite) have proven to be compelling candidates for charge-storage materials in high-density batteries. In particular, the tunnels can support one-dimensional chains of K$^+$ ions (which act as structure-stabilizing dopants) and H$_2$O molecules, as these chains are favored by strong H-bonds and electrostatic interactions. In this work, we examine the role of water molecules in enhancing the stability of K$^+$-doped $α-$Mn$_8$O$_{16}$ (cryptomelane). The combined experimental and theoretical analyses show that for high enough concentrations of water and tunnel-ions, H$_2$O displaces K$^+$ ions from their natural binding sites. This displacement becomes energetically favorable due to the formation of K$_2^+$ dimers, thereby modifying the stoichiometric charge of the system. These findings have potentially significant technological implications for the consideration of cryptomelane as a Li$^+$/Na$^+$ battery electrode. Our work establishes the functional role of water in altering the energetics and structural properties of cryptomelane, an observation that has frequently been overlooked in previous studies.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Critical current density and vortex pinning in tetragonal FeS$_{1-x}$Se$_{x}$ ($x=0,0.06$)

We report critical current density ($J_c$) in tetragonal FeS single crystals, similar to iron based superconductors with much higher superconducting critical temperatures ($T_{c}$'s). The $J_c$ is enhanced 3 times by 6\% Se doping. We observe scaling of the normalized vortex pinning force as a function of reduced field at all temperatures. Vortex pinning in FeS and FeS$_{0.94}$Se$_{0.06}$ shows contribution of core-normal surface-like pinning. Reduced temperature dependence of $J_c$ indicates that dominant interaction of vortex cores and pinning centers is via scattering of charge carriers with reduced mean free path ($δ$$l$), in contrast to K$_x$Fe$_{2-y}$Se$_2$ where spatial variations in $T_{c}$ ($δ$$T_{c}$) prevails.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

High spatial frequency periodic structures induced on ferric ion-doped Polyvinyl Pyrrolidone film by femtosecond laser pulses

Utilizing continues-wave or pulsed laser to induce nano-structures on various material surfaces is one significant method in nano-fabrication technology. In this report, we investigate the formation of high spatial frequency periodic structures on Polyvinyl Pyrrolidone (PVP) film by a linearly polarized femtosecond laser. Ferric (Fe) ions are introduced into the film to improve the photosensitivity. Regular nano-gratings with spatial periods at the range of 60-100nm, which are about one tenth of the irradiating wavelength, can be induced. The period direction of the nano-gratings is perpendicular to the polarization of the femtosecond laser. By tuning the laser energy and scanning speed, we find that the nano-gratings can be formed in a wide range of experimental parameters. As high laser energy can excite not only metals, but also semiconductors and polymers, we believe the formation of the nano-gratings is due to the interaction between the incident femtosecond laser and surface plasmons. The laser processable PVP-based materials and the induced nano-gratings will have potential applications in biophotonics and nanophotonics.

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preprint2016arXiv

Interlayer electronic transport in CaMnBi$_{2}$ antiferromagnet

We report interlayer electronic transport in CaMnBi$_{2}$ single crystals. Quantum oscillations and angular magnetoresistance suggest coherent electronic conduction and valley polarized conduction of Dirac states. Small cyclotron mass, large mobility of carriers and nontrivial Berry's phase are consistent with the presence of Dirac fermions on the side wall of the warped cylindrical Fermi surface. Similar to SrMnBi$_{2}$ that features an anisotropic Dirac cone, our results suggest that magnetic field-induced changes in the interlayer conduction are also present in layered bismuth-based materials with zero-energy line in momentum space created by the staggered alkaline earth atoms.

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preprint2016arXiv

Two-dimensional Dirac fermions in YbMnBi2 antiferromagnet

We report two-dimensional quantum transport and Dirac fermions in YbMnBi2 single crystals. YbMnBi2 is a layered material with anisotropic conductivity and magnetic order below 290 K. Magnetotransport properties, nonzero Berry phase and small cyclotron mass indicate the presence of quasi two dimensional Dirac fermions. Quantum oscillations in Hall resistivity suggest the presence of both electron and hole parts of the Fermi surface whereas the Berry phase suggests spin-orbit coupling.

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preprint2015arXiv

Graphene-Silicon Layered Structures on Single-crystalline Ir(111) Thin Films

Single-crystalline transition metal films are ideal playing fields for the epitaxial growth of graphene and graphene-base materials. Graphene-silicon layered structures were successfully constructed on Ir(111) thin film on Si substrate with an yttria-stabilized zirconia buffer layer via intercalation approach. Such hetero-layered structures are compatible with current Si-based microelectronic technique, showing high promise for applications in future micro- and nano-electronic devices.

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preprint2015arXiv

Low-Dimensional Conduction Mechanisms in Highly Conductive and Transparent Conjugated Polymers

Electronic conduction in conjugated polymers is of emerging technological interest for high-performance optoelectronic and thermoelectric devices. A completely new aspect and understanding of the conduction mechanism on conducting polymers is introduced, allowing the applicability of materials to be optimized. The charge-transport mechanism is explained by direct experimental evidence with a very well supported theoretical model.

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preprint2015arXiv

Parallel Stitching of Two-Dimensional Materials

Diverse parallel stitched two-dimensional heterostructures are synthesized, including metal-semiconductor (graphene-MoS2), semiconductor-semiconductor (WS2-MoS2), and insulator-semiconductor (hBN-MoS2), directly through selective sowing of aromatic molecules as the seeds in chemical vapor deposition (CVD) method. Our methodology enables the large-scale fabrication of lateral heterostructures with arbitrary patterns, and clean and precisely aligned interfaces, which offers tremendous potential for its application in integrated circuits.

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preprint2015arXiv

Proximity Driven Enhanced Magnetic Order at Ferromagnetic Insulator / Magnetic Topological Insulator Interface

Magnetic exchange driven proximity effect at a magnetic insulator / topological insulator (MI/TI) interface provides a rich playground for novel phenomena as well as a way to realize low energy dissipation quantum devices. Here we report a dramatic enhancement of proximity exchange coupling in the MI / magnetic-TI EuS / Sb$_{2-x}$V$_x$Te$_3$ hybrid heterostructure, where V doping is used to drive the TI (Sb$_{2}$Te$_3$) magnetic. We observe an artificial antiferromagnetic-like structure near the MI/TI interface, which may account for the enhanced proximity coupling. The interplay between the proximity effect and doping provides insights into controllable engineering of magnetic order using a hybrid heterostructure.

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preprint2015arXiv

Shear-Exfoliated Phosphorene for Rechargeable Nanoscale Battery

Discovery of atomically thin black phosphorus (called phosphorene) holds promise to be used as an alternative two-dimensional material to graphene and transition metal dichalcogenides especially as an anode material for lithium-ion batteries (LIBs). However, at present bulk black phosphorus (BP) still suffers from rapid capacity fading that results in poor rechargeable performance. Here, for the first time, we use in situ transmission electron microscopy (TEM) to construct nanoscale phosphorene LIBs and visualize the capacity fading mechanism in thick multilayer phosphorene by real time capturing delithiation-induced structural decomposition that reduces electrical conductivity and thus causes irreversibility of lithiated Li3P phase. We further demonstrate that few-layer phosphorene successfully circumvents the structural decomposition and holds superior structural restorability, even subjected to multi-cycle lithiation/delithiation processes and concomitant huge volume expansion. This finding affords new experimental insights into thickness-dependent lithium diffusion kinetics in phosphorene. Additionally, a scalable liquid-phase shear exfoliation route has been developed to produce high-quality ultrathin (monolayer or few-layer) phosphorene, only by a high-speed shear mixer or even a household kitchen blender with the shear rate threshold, which will pave the way for potential large-scale applications in LIBs once the rechargeable phosphorene nanoscale batteries can be transferred to industrialized enlargement in the future.

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preprint2014arXiv

Calibration and data collection protocols for reliable lattice parameter values in electron pair distribution function (ePDF) studies

We explore and describe different protocols for calibrating electron pair distribution function (ePDF) measurements for quantitative studies on nano-materials. We find the most accurate approach to determine the camera-length is to use a standard calibration sample of Au nanoparticles from National Institute of Standards and Technology. Different protocols for data collection are also explored, as are possible operational errors, to find the best approaches for accurate data collection for quantitative ePDF studies.

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preprint2014arXiv

Enhanced spin-orbit interaction and Kondo scattering in $δ$-doped LaTiO$_3$/SrTiO$_3$ interfaces

We present a study of delta ($δ$) doping at LaTiO$_3$/SrTiO$_3$ (LTO/STO) interface with iso-structural antiferromagnetic perovskite LaCrO$_3$ (LCO) that dramatically alters the properties of the two dimensional electron gas (2-DEG) at the interface. The effects include a reduction in sheet-carrier density, prominence of the low temperature resistivity minimum, enhancement of weak antilocalization below 10 K and observation of a strong anisotropic magnetoresistance (MR). The positive and negative MR for out-of-plane and in-plane field respectively and the field and temperature dependencies of MR suggest Kondo scattering by localized Ti$^{3+}$ moments renormalized by spin-orbit interaction at T $<$ 10 K, with the increased $δ$-layer thickness. Electron energy loss spectroscopy and density functional calculations provide convincing evidence for blocking of electron transfer from LTO to STO by the $δ$-layer.

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preprint2014arXiv

Evolution Process of Wurtzite ZnO Films on Cubic MgO (001) Substrates: a Structural, Optical and Electronic Investigation of the Misfit Structures

The interface between hexagonal ZnO films and cubic MgO (001) substrates, fabricated through molecular beam epitaxy, are thoroughly investigated. X-ray diffraction and (scanning) transmission electron microscopy reveal that, at the substrate temperature above 200 degree C, the growth follows the single [0001] direction; while at the substrate below 150 degree C, the growth is initially along [0001] and then mainly changes to [0-332] variants beyond the thickness of about 10 nm. Interestingly, a double-domain feature with a rotational angle of 30 degree appears for the growth along [0001] regardless of the growth temperature, experimentally demonstrated the theoretical predictions for occurrence of double rotational domains in such a heteroepitaxy [Grundmann et al, Phys. Rev. Lett. 105, 146102 (2010)]. It is also found that, the optical transmissivity of the ZnO film is greatly influenced by the mutation of growth directions, stimulated by the bond-length modulations, as further determined by X-ray absorption Spectra (XAS) at Zn K edge. The XAS results also show the evolution of 4pxy and 4pz states in the conduction band as the growth temperature increases. The results obtained from this work can hopefully promote the applications of ZnO in advanced optoelectronics for which its integration with other materials of different phases is desirable.

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preprint2014arXiv

Experimental Verification of Van Vleck Nature of Long-Range Ferromagnetic Order in Vanadium-Doped Three-Dimensional Topological Insulator Sb$_{2}$Te$_{3}$

We demonstrate by high resolution low temperature electron energy loss spectroscopy (EELS) measurements that the long range ferromagnetic (FM) order in vanadium (V)-doped topological insulator Sb$_2$Te$_3$ has the nature of van Vleck-type ferromagnetism. The positions and the relative amplitudes of two core-level peaks (L$_3$ and L$_2$) of the V EELS spectrum show unambiguous change when the sample is cooled from room temperature to T=10K. Magnetotransport and comparison of the measured and simulated EELS spectra confirm that these changes originate from onset of FM order. Crystal field analysis indicates that in V-doped Sb$_2$Te$_3$, partially filled core states contribute to the FM order. Since van Vleck magnetism is a result of summing over all states, this magnetization of core level verifies the van Vleck-type ferromagnetism in a direct manner.

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preprint2014arXiv

Strong Coupling of the Iron-Quadrupole and Anion-Dipole Polarizations in Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$

We use a quantitative convergent beam electron diffraction (CBED) based method to image the valence electron density distribution in Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$. We show a remarkable increase in both the charge quadrupole of the Fe cations and the charge dipole of the arsenic anions upon Co doping from $x=0$ ($T_c=0$ K) to $x=0.1$ ($T_c=22.5$ K). Our data suggest that an unexpected electronic correlation effect, namely strong coupling of Fe orbital fluctuation and anion electronic polarization, is present in iron-based superconductors.

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preprint2014arXiv

Tunable THz Surface Plasmon Polariton based on Topological Insulator-Layered Superconductor Hybrid Structure

We theoretically investigate the surface plasmon polariton (SPP) at the interface between 3D strong topological insulator (TI) and layered superconductor-magnetic insulator structure. The tunability of SPP through electronic doping can be enhanced when the magnetic permeability of the layered structure becomes higher. When the interface is gapped by superconductivity or perpendicular magnetism, SPP dispersion is further distorted, accompanied by a shift of group velocity and penetration depth. Such a shift of SPP reaches maximum when the magnitude of Fermi level approaches the gap value, and may lead to observable effects. The tunable SPP at the interface between layered superconductor and magnetism materials in proximity to TI surface may provide new insight in the detection of Majorana Fermions.

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preprint2013arXiv

Topological Effect of Surface Plasmon Excitation in Gapped Isotropic Topological Insulator Nanowires

We present a theoretical investigation of the surface plasmon (SP) at the interface between topologically non-trivial cylindrical core and topological-trivial surrounding material, from the axion electrodynamics and modified constitutive relations. We find that the topological effect always leads to a red-shift of SP energy, while the energy red-shift decreases monotonically as core diameter decreases. A qualitative picture based on classical perturbation theory is given to explain these phenomena, from which we also infer that in order to enhance the shift, the difference between the inverse of dielectric constants of two materials shall be increased. We also find that the surrounding magnetic environment suppresses the topological effect. All these features can be well described by a simple ansatz surface wave, which is in good agreement with full electromagnetic eigenmodes. In addition, bulk plasmon energy at ω_{P}=17.5\pm0.2eV for semiconducting Bi2Se3 nanoparticle is observed from high-resolution Electron Energy Loss Spectrum Image measurements.

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preprint2013arXiv

Two-dimensional electron-gas-like charge transport at magnetic Heusler alloy-SrTiO$_3$ interface

We report remarkably low residual resistivity, giant residual resistivity ratio, free-electron-like Hall resistivity and high mobility ($\approx$ 10$^4$ cm$^2$V$^{-1}$s$^{-1}$) charge transport in epitaxial films of Co$_2$MnSi and Co$_2$FeSi grown on (001) SrTiO$_3$. This unusual behavior is not observed in films deposited on other cubic oxide substrates of comparable lattice parameters. The scaling of the resistivity with thickness of the films allow extraction of interface conductance, which can be attributed to a layer of oxygen vacancies confined within 1.9 nm of the interface as revealed by atomically resolved electron microscopy and spectroscopy. The high mobility transport observed here at the interface of a fully spin polarized metal is potentially important for spintronics applications.

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preprint2011arXiv

Polarization manipulation holographic lithography by single refracting prism

We propose a simple but effective strategy for polarization manipulation holographic lithography by single refractive prism. By tuning the polarization of single laser beam, we simply obtain a pill shape interference pattern where multiple modulated beams are needed in conventional holography lithography. Fabrication of large area pill shape two-dimensional polymer photonic crystals template using one beam and one shoot holography lithography is shown as an example. This integrated fabrication technique, for example, can release the crucial stability restrictions imposed on the multiple beams holography lithography.

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preprint2010arXiv

Nanostructures and Defects in Non-equilibrium Synthesized Filled Skutterudite CeFe4Sb12

We studied nanoprecipitates and defects in p-type filled skutterudite CeFe4Sb12 prepared by non-equilibrium melt-spinning plus spark plasma sintering method using transmission electron microscopy. Nanoprecipitates with mostly spherical shapes and different sizes (from several nm to several tens of nm) have been observed. The most typically observed nanoprecipitates are shown to be Sb-rich. Superlattices with a periodicity of about 3.576 nm were induced by the ordering of excessive Sb atoms along the c direction. These nanoprecipitates usually share coherent interfaces with the surrounding matrix and induce anisotropic and strong strain fields in the surrounding matrix. Nanoprecipitates with compositions close to CeSb2 are much larger in size (~ 30 nm) and have orthorhombic structures. Various defects were typically observed on the interfaces between these nanoprecipitates and the matrix. The strain fields induced by these nanoprecipitates are less distinct, possibly because part of the strains has been released by the formation of defects.

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