Bing Liu, Jian Liu, Guangyao Miao, Siwei Xue, Shuyuan Zhang, Lixia Liu, Xiaochun Huang, Xuetao Zhu, Sheng Meng, Jiandong Guo, Miao Liu, Weihua Wang
The intriguing properties, especially Dirac physics in graphene, have inspired the pursuit of two-dimensional materials in honeycomb structure. Here we achieved a monolayer transition metal monochalcogenide AgTe on Ag(111) by tellurization of the substrate. High-resolution scanning tunneling microscopy, combined with low-energy electron diffraction, angle-resolved photoemission spectroscopy, and density functional theory calculations, demonstrates the planar honeycomb structure of AgTe. The first principle calculations further reveal that, protected by the in-plane mirror reflection symmetry, two Dirac node-line Fermions exist in the electronic structures of free-standing AgTe when spin-orbit coupling (SOC) is ignored. While in fact the SOC leads to the gap opening, and resulting in the emergence of the topologically nontrivial quantum spin Hall edge state. Importantly, our experiments evidence the chemical stability of the monolayer AgTe in ambient conditions. It is possible to study AgTe by more ex-situ measurements and even to apply it in novel electronic devices.
Tenglong Lu, Sheng Meng, Miao Liu
Sodium-ion batteries are a viable alternative to lithium-ion technology due to the plentiful sodium resources. However, certain commercialization challenges, such as low specific energies and poor cycling performance of current Na-ion cathodes, still need to be addressed. To overcome these hurdles, this study explored the potential of a novel class of fluoride-based materials, specifically trigonal-type Na$_2$MM'F$_7$ (M and M' are redox-active metals) belonging to the weberite-type compounds, as promising candidates for Na-ion cathodes. Through a comprehensive assessment utilizing ab initio calculations, twelve prospective compounds were identified, demonstrating high thermodynamic stability, large gravimetric capacities (>170 mAh/g), and low net Na-ion migration barriers (<600 meV). Significantly, ten out of the twelve screened compounds exhibit high specific energies exceeding 580 Wh/kg (approximately equals to the specific energy of LiFePO$_4$), indicating their exceptional electrochemical performance. This study will pave the way for further advancements in fluoride-based electrode materials.
Fangda Wei, Miao Liu, Yingxue Wang, Jing Wang, Shenghui Zhao, Nan Li
Audio-visual deepfake detection typically employs a complementary multi-modal model to check the forgery traces in the video. These methods primarily extract forgery traces through audio-visual alignment, which results from the inconsistency between audio and video modalities. However, the traditional multi-modal forgery detection method has the problem of insufficient feature extraction and modal alignment deviation. To address this, we propose a multi-scale cross-modal transformer encoder (MSCT) for deepfake detection. Our approach includes a multi-scale self-attention to integrate the features of adjacent embeddings and a differential cross-modal attention to fuse multi-modal features. Our experiments demonstrate competitive performance on the FakeAVCeleb dataset, validating the effectiveness of the proposed structure.
Miao Liu, Dexin Yang, Yan Zhang, Zhaopeng Cui, James M. Rehg, Siyu Tang
We introduce a novel task of reconstructing a time series of second-person 3D human body meshes from monocular egocentric videos. The unique viewpoint and rapid embodied camera motion of egocentric videos raise additional technical barriers for human body capture. To address those challenges, we propose a simple yet effective optimization-based approach that leverages 2D observations of the entire video sequence and human-scene interaction constraint to estimate second-person human poses, shapes, and global motion that are grounded on the 3D environment captured from the egocentric view. We conduct detailed ablation studies to validate our design choice. Moreover, we compare our method with the previous state-of-the-art method on human motion capture from monocular video, and show that our method estimates more accurate human-body poses and shapes under the challenging egocentric setting. In addition, we demonstrate that our approach produces more realistic human-scene interaction.
Yutao Jiang, Ze Yu, Yuxin Wang, Tenglong Lu, Sheng Meng, Kun Jiang, Miao Liu
CsV3Sb5 kagome lattice holds the promise for manifesting electron correlation, topology and superconducting. However, by far only three CsV3Sb5-like kagome materials have been experimentally spotted. In this work, we enlarge this family of materials to 1386 compounds via element species substitution, and the further screening process suggests that 28 promising candidates have superior thermodynamic stability, hence they are highly likely to be synthesized. Moreover, these compounds possess several identical electronic structures, and can be categorized into five non-magnetic and three magnetic groups accordingly. It is our hope that this work can greatly expand the viable phase space of the CsV3Sb5-like materials for investigating or tuning the novel quantum phenomena in kagome lattice.
Miao Liu, Marlos C. Machado, Gerald Tesauro, Murray Campbell
Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient exploration. Despite its initial promising results, a couple of issues in current algorithms limit its application, namely: (1) EO methods require two separate steps (eigenoption discovery and reward maximization) to learn a control policy, which can incur a significant amount of storage and computation; (2) EOs are only defined for problems with discrete state-spaces and; (3) it is not easy to take the environment's reward function into consideration when discovering EOs. To addresses these issues, we introduce an algorithm termed eigenoption-critic (EOC) based on the Option-critic (OC) framework [Bacon17], a general hierarchical reinforcement learning (RL) algorithm that allows learning the intra-option policies simultaneously with the policy over options. We also propose a generalization of EOC to problems with continuous state-spaces through the Nyström approximation. EOC can also be seen as extending OC to nonstationary settings, where the discovered options are not tailored for a single task.
Decai Yu, Elizabeth M. Lupton, Miao Liu, Wei Liu, Feng Liu
We investigate the magnetic properties of nano-holes (NHs) patterned in graphene using first principles calculations. We show that superlattices consisting of a periodic array of NHs form a new family of 2D crystalline "bulk" magnets whose collective magnetic behavior is governed by inter-NH spin-spin interaction. They exhibit long-range magnetic order well above room temperature. Furthermore, magnetic semiconductors can be made by doping magnetic NHs into semiconducting NH superlattices. Our findings offer a new material system for fundamental studies of spin-spin interaction and magnetic ordering in low dimensions, and open up the exciting opportunities of making engineered magnetic materials for storage media and spintronics applications.
Hanwen Kang, Tenglong Lu, Zhanbin Qi, Jiandong Guo, Sheng Meng, Miao Liu
We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy surface sampling and interpolation. Our method suppresses periodic self interactions via supercell expansion, builds a continuous PES from MLFF energies on a spatial grid, and extracts minimum energy pathways without predefined NEB images. Across twelve benchmark electrode and electrolyte materials including LiCoO2, LiFePO4, and LGPS our MLFF-derived barriers lie within tens of meV of DFT and experiment, while achieving ~10^2 x speedups over DFT-NEB. We benchmark GPTFF, CHGNet, and MACE, show that fine-tuning on PBE/PBE+U data further enhances accuracy, and provide an open-source package for high-throughput materials screening and interactive PES visualization.
Miao Liu, Fangda Wei, Jing Wang, Xinyuan Qian
Existing deepfake detection research has primarily focused on scenarios where the manipulated subject is actively speaking, i.e., generating fabricated content by altering the speaker's appearance or voice. However, in realistic interaction settings, attackers often alternate between falsifying speaking and listening states to mislead their targets, thereby enhancing the realism and persuasiveness of the scenario. Although the detection of 'listening deepfakes' remains largely unexplored and is hindered by a scarcity of both datasets and methodologies, the relatively limited quality of synthesized listening reactions presents an excellent breakthrough opportunity for current deepfake detection efforts. In this paper, we present the task of Listening Deepfake Detection (LDD). We introduce ListenForge, the first dataset specifically designed for this task, constructed using five Listening Head Generation (LHG) methods. To address the distinctive characteristics of listening forgeries, we propose MANet, a Motion-aware and Audio-guided Network that captures subtle motion inconsistencies in listener videos while leveraging speaker's audio semantics to guide cross-modal fusion. Extensive experiments demonstrate that existing Speaking Deepfake Detection (SDD) models perform poorly in listening scenarios. In contrast, MANet achieves significantly superior performance on ListenForge. Our work highlights the necessity of rethinking deepfake detection beyond the traditional speaking-centric paradigm and opens new directions for multimodal forgery analysis in interactive communication settings. The dataset and code are available at https://anonymous.4open.science/r/LDD-B4CB.
Miao Liu, Yong Han, Lin Tang, Jin-Feng Jia, Qi-Kun Xue, Feng Liu
We develop a theoretical framework to investigate the interplay between quantum size effect (QSE) and strain effect on the stability of metal nanofilms. The QSE and strain effect are shown to be coupled through the concept of "quantum electronic stress. First-principles calculations reveal large quantum oscillations in the surface stress of metal nanofilms as a function of film thickness. This adds extrinsically additional strain-coupled quantum oscillations to surface energy of strained metal nanofilms. Our theory enables a quantitative estimation of the amount of strain in experimental samples, and suggests strain be an important factor contributing to the discrepancies between the existing theories and experiments.
Taposh Banerjee, Miao Liu, Jonathan P. How
Optimal control in non-stationary Markov decision processes (MDP) is a challenging problem. The aim in such a control problem is to maximize the long-term discounted reward when the transition dynamics or the reward function can change over time. When a prior knowledge of change statistics is available, the standard Bayesian approach to this problem is to reformulate it as a partially observable MDP (POMDP) and solve it using approximate POMDP solvers, which are typically computationally demanding. In this paper, the problem is analyzed through the viewpoint of quickest change detection (QCD), a set of tools for detecting a change in the distribution of a sequence of random variables. Current methods applying QCD to such problems only passively detect changes by following prescribed policies, without optimizing the choice of actions for long term performance. We demonstrate that ignoring the reward-detection trade-off can cause a significant loss in long term rewards, and propose a two threshold switching strategy to solve the issue. A non-Bayesian problem formulation is also proposed for scenarios where a Bayesian formulation cannot be defined. The performance of the proposed two threshold strategy is examined through numerical analysis on a non-stationary MDP task, and the strategy outperforms the state-of-the-art QCD methods in both Bayesian and non-Bayesian settings.
Miao Liu, Jinyang Liang, Fiorenzo Vetrone
The near-infrared (NIR) emission of rare-earth doped nanoparticles (RENPs), known as downshifting luminescence, has been extensively investigated in diverse applications from information technology to biomedicine. In promoting brightness and enriching the functionalities of the downshifting luminescence of RENPs, numerous studies have exploited inert shell to protect rare-earth dopants from surface quenchers. However, internal concentration quenching remains an unsolved puzzle when using higher dopant concentrations of rare-earth ions in an attempt to obtain brighter emission. Following a plethora of research involving core-shell structures, the interface has shown to be controllable, ranging from a well-defined, abrupt boundary to an obscure one with cation intermixing. By utilizing this inter-mixed core-shell property for the first time, we design a new architecture to create a homogeneous double-layer core-shell interface to extend the active layer, allowing more luminescent centers without severe concentration quenching. By systematically deploying the crystallinity of the starting core, shell growth dynamics, and dopant concentrations, the downshifting luminescence intensity of new archictecture achieves a 12-fold enhancement surpassing the traditional core-shell structure. These results provide deeper insight into the potential benefits of the intermixed core-shell structure, offering an effective approach to tackling the internal concentration quenching effect for highly boosted NIR optical performance.
Miao Liu, Feng Liu
Generally, there are two distinct effects in modifying the properties of low-dimensional nanostructures: surface effect (SS) due to increased surface-volume ratio and quantum size effect (QSE) due to quantum confinement in reduced dimension. The SS has been widely shown to affect the elastic constants and mechanical properties of nanostructures. Here, using Pb nanofilm and graphene nanoribbon as model systems, we demonstrate the QSE on the elastic constants of nanostructures by first-principles calculations. We show that generally QSE is dominant in affecting the elastic constants of metallic nanostructures while SS is more pronounced in semiconductor and insulator nanostructures. Our findings have broad implications in quantum aspects of nanomechanics.
Miao Liu, Sheng Meng
A close look of Google's GNoME inorganic materials dataset [Nature 624, 80 (2023)], and 11 things you would like to know.
Guanghui Cai, Yutao Jiang, Hui Zhou, Ze Yu, Kun Jiang, Youguo Shi, Sheng Meng, Miao Liu
Finding viable Kagome lattices is vital for materializing novel phenomena in quantum materials. In this work, we performed element substitutions on CsV3Sb5 with space group P6/mmm, TbMn6Sn6 with space group P6/mmm, and CsV6Sb6 with space group R-3 m, respectively, as the parent compounds. A total of 4158 materials were obtained through element substitutions, and these materials were then calculated via density function theory in high-throughput mode. Afterward, 48 materials were identified with high thermodynamic stability (E_hull<5meV/atom). Furthermore, we compared the thermodynamic stability of three different phases with the same elemental composition and predicted some competing phases that may arise during material synthesis. Finally, by calculating the electronic structures of these materials, we attempted to identify patterns in the electronic structure variations as the elements change. This work provides guidance for discovering promising AM3X5/AM6X6 Kagome materials from a vast phase space.
Tenglong Lu, Sheng Meng, Miao Liu
This paper presents the calculation results of electron-phonon interactions within the LuH$_2$, LuH$_3$, and LuN systems under 0 GPa and 10 GPa via density functional theory at the GGA-PBE level. The purpose of this work is to provide useful data that may be of the interests of the superconducting community as it was reported that the Lu-H-N compound is likely to be a room-temperature superconductor under 1 GPa [Nature, 615, 244 (2023)].
Zhendong Cao, Guanghui Cai, Fankai Xie, Huaxian Jia, Wei Liu, Yaxian Wang, Feng Liu, Xinguo Ren, Sheng Meng, Miao Liu
DFT+U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes even qualitative failures of local and semilocal approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system is non-trivial and usually requires computationally intensive and cumbersome first-principles calculations. In this Letter, we address this issue by building a machine learning (ML) model to predict material-specific U parameters only from the structural information. An ML model is trained for the Mn-O chemical system by calibrating their DFT+U electronic structures with the hybrid functional results of more than Mn-O 3000 structures. The model allows us to determine a reliable U value (MAE=0.128 eV, R2=0.97) for any given structure at nearly no computational cost; yet the obtained U value is as good as that obtained from the conventional first-principles methods. Further analysis reveals that the U value is primarily determined by the local chemical structure, especially the bond lengths, and this property is well captured by the ML model developed in this work. This concept of the ML U model is universally applicable and can considerably ease the usage of the DFT+U method by providing structure-specific, readily accessible U values.
Miao Liu, Siyu Tang, Yin Li, James Rehg
We address the challenging task of anticipating human-object interaction in first person videos. Most existing methods ignore how the camera wearer interacts with the objects, or simply consider body motion as a separate modality. In contrast, we observe that the international hand movement reveals critical information about the future activity. Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action. Specifically, we consider the future hand motion as the motor attention, and model this attention using latent variables in our deep model. The predicted motor attention is further used to characterise the discriminative spatial-temporal visual features for predicting actions and interaction hotspots. We present extensive experiments demonstrating the benefit of the proposed joint model. Importantly, our model produces new state-of-the-art results for action anticipation on both EGTEA Gaze+ and the EPIC-Kitchens datasets. Our project page is available at https://aptx4869lm.github.io/ForecastingHOI/
Miao Liu, Xin Chen, Yun Zhang, Yin Li, James M. Rehg
We address the challenging problem of learning motion representations using deep models for video recognition. To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition. Specifically, we propose to leverage output attention maps as a vehicle to transfer the learned representation from a motion (flow) network to an RGB network. We systematically study the design of attention modules, and develop a novel method for attention distillation. Our method is evaluated on major action benchmarks, and consistently improves the performance of the baseline RGB network by a significant margin. Moreover, we demonstrate that our attention maps can leverage motion cues in learning to identify the location of actions in video frames. We believe our method provides a step towards learning motion-aware representations in deep models. Our project page is available at https://aptx4869lm.github.io/AttentionDistillation/
Yingzong Liang, Mingwei Chen, Yanan Wang, Huaxian Jia, Tenglong Lu, Fankai Xie, Sheng Meng, Miao Liu
Harnessing the recent advance in data science and materials science, it is feasible today to build predictive models for materials properties. In this study, we employ the data of high-throughput quantum mechanics calculations based on 170,714 inorganic crystalline compounds to train a machine learning model for formation energy prediction. Different from the previous work, our model reaches a fairly good predictive ability (R2=0.982 and MAE=0.07 eVatom-1, DenseNet model) and meanwhile can be universally applied to the large phase space of inorganic materials. The improvement comes from several effective structure-dependent descriptors that are proposed to take the information of electronegativity and structure into account. This model can provide a useful tool to search for new materials in a vast phase space in a fast and cost-effective manner.